DE102019009243B3 - Sensor system with transmission of the ultrasonic signal to the computer system by means of approximating signal object compression and decompression - Google Patents

Abstract

sensor system
- with at least one computer system and
- with at least one sensor,
- Wherein the sensor system carries out a method for transmitting sensor data, in particular an ultrasonic sensor, from the sensor to the computer system, in particular in a vehicle, and
- wherein the sensor executes a method, hereinafter referred to as a compression method, which has or comprises the following steps:
• sending out an ultrasonic burst;
• receiving an ultrasonic signal and forming an ultrasonic received signal;
• forming an ultrasonic echo signal (1) from the received ultrasonic signal;
• Storing the ultrasonic echo signal (1) in the form of digital samples in a memory (601);
• subtracting a reconstructed ultrasonic echo signal model (610) stored in a reconstruction memory (603) from the previously received ultrasonic echo signal (1) to form a residual signal (660);
• forming a feature vector signal (138) from the residual signal (660);
• Evaluation of at least time sections (Hamming windows) of the feature vector signal (138)
• by forming at least one binary, digital or analog distance value between the feature vector signal and one or more signal object prototype values for recognizable signal object classes and assigning a recognizable signal object class as a recognized signal object if the absolute value of the distance value is one or more predetermined, binary, digital or analog Distance values falls below in terms of amount, and / or
• by assigning a recognizable signal object class as a recognized signal object, in particular by means of an estimator (151), with a neural network model and/or with a Petri network;
• Recognition of signal objects and classification of these signal objects into assigned signal object classes within the ultrasonic received signal (1),
• where a signal object class can also include only one signal object and
• wherein each signal object (122) recognized and classified in this way is assigned at least one assigned signal object parameter and a symbol corresponding to the signal object class assigned to this signal object, or
• wherein each thus recognized and classified signal object (122) has at least one assigned signal object parameters and a symbol for this signal object are determined;
• Generating the reconstructed ultrasonic echo signal model (610) from the summing linearly superimposed signal curve models of the detected signal objects;
• Storing the ultrasonic echo signal model (610) in the reconstruction memory (603)
• Ending this reducing classification of the ultrasonic echo signal (1) into signal objects using the ultrasonic echo signal model (610) if the magnitudes of the sampled values of the residual signal (660) are below the magnitudes of a predetermined threshold value curve and
• Transmission of at least the symbol of a recognized signal object class and at least one associated signal object parameter of this recognized signal object class to the computer system and
- wherein the computer system executes a decompression method for decompressing ultrasonic reception data compressed in this signal-object-oriented manner and
- wherein the computer system receives the data from the sensor and
- where the decompression procedure comprises the following steps:
• receiving the data to be decompressed in the form of the transmitted symbols of the recognized signal object classes and the associated signal object parameters of these recognized signal object classes;
• providing an ultrasonic echo signal model that has no signal;
• Supplementing the ultrasonic echo signal model by adding a prototypical parameterized signal curve of a signal object that is contained in the ultrasonic reception data;
• Forming the reconstructed ultrasonic received signal, which can be done in the form of forming samples of a reconstructed envelope;
• Using the reconstructed ultrasonic received signal.

Description

field of invention

The invention relates to a method for transmitting data from an ultrasonic sensor system for parking aids in vehicles to a computer system and a corresponding device. The basic idea is the detection of potentially relevant structures in the measurement signal and the compression of this measurement signal by transmitting only these identified, potentially relevant structures instead of the measurement signal itself. The actual detection of objects, e.g. obstacles to the parking process, only takes place after the measurement signal has been reconstructed as a reconstructed measurement signal in the computer system, where typically several such decompressed measurement signals from several ultrasonic sensor systems come together. Here the decompression of the data in the control unit after the compression in the sensor is claimed by structure recognition in the measurement signal.

General introduction

Ultrasonic sensor systems for parking aids in vehicles are enjoying increasing popularity. Here, there is a desire to transfer more and more data from the actual measurement signal of the ultrasonic sensor to a central computer system, where it is processed with data from other ultrasonic sensor systems and also from other types of sensor systems, e.g. There is therefore a desire not to carry out object recognition in the ultrasonic sensor system itself, but only through this sensor fusion in the computer system in order to avoid data loss and thus reduce the probability of incorrect information and thus incorrect decisions and thus accidents. At the same time, however, the transmission bandwidth of the available sensor data buses is limited. Their replacement should be avoided as they have proven themselves in the field. At the same time, therefore, there is a desire not to allow the amount of data to be transmitted to increase. In short: the information content of the data and its relevance for the subsequent obstacle detection (object detection) running in the computer system must be increased without having to increase the data rate too much - better: without having to increase the data rate at all. On the contrary: the data rate requirement should preferably even be reduced in order to allow data rate capacities for the transmission of status data and self-test information from the ultrasonic sensor system to the computer system, which is absolutely necessary in the context of functional safety (FuSi). The disclosure presented here addresses this problem.

Various methods of processing an ultrasonic sensor signal are already known in the prior art.

From the WO 2012/016 834 A1 such a method for evaluating an echo signal for vehicle environment detection is known, for example. It is proposed there to transmit a measurement signal with a definable coding and form and to search for the components of the measurement signal in the reception signal in the reception signal by means of a correlation with the measurement signal and to determine them. The level of the correlation and not the level of the envelope of the echo signal is then evaluated using a threshold value.

From the DE 4 433 957 A1 It is known to emit ultrasonic pulses periodically for obstacle detection and to infer the position of obstacles from the propagation time, echoes that remain correlated over several measuring cycles being reinforced during the evaluation, while echoes that remain uncorrelated are suppressed.

From the DE 10 2012 015 967 A1 a method for decoding a received signal received by an ultrasonic sensor of a motor vehicle is known, in which a transmitted signal of the ultrasonic sensor is sent out in coded form and for decoding the received signal is correlated with a reference signal, with a frequency shift of the received signal compared to the reference signal before correlating the received signal with the reference signal transmission signal is determined and the reception signal is correlated with the transmission signal whose frequency is shifted by the determined frequency shift as a reference signal, the same reception signal being subjected to a Fourier transformation to determine the frequency shift of the reception signal and the frequency shift being determined on the basis of a result of the Fourier transformation.

From the DE 10 2011 085 286 A1 is a method for detecting the area around a vehicle using ultrasound, with ultrasonic pulses being emitted and the ultrasonic echoes reflected from objects being detected, characterized in that the detection range is divided into at least two distance ranges, with the ultrasonic pulses used for detection in the respective distance range being transmitted independently of one another and are encoded by different frequencies.

From the WO 2014 108 300 A1 a device and a method for environment sensors using a signal converter and an evaluation unit are known, signals received from the environment with a first impulse response length at a first point in time during a measurement cycle and with a second, longer impulse response length at a second later point in time within the same measurement cycle, signals are filtered depending on the transit time .

From the DE 10 2015 104 934 A1 a method for providing information that depends on an object detected in an area surrounding a motor vehicle is known. In the process of DE 10 2015 104 934 A1 the surrounding area of the motor vehicle is detected with a sensor device of the motor vehicle and information is provided at a communication interface in the motor vehicle. Raw sensor data are stored in a sensor device-side control unit as information about a free space detected between the sensor device and an object detected in the surrounding area. These sensor raw data are according to the technical teaching of DE 10 2015 104 934 A provided at a communication interface connected to the control unit on the sensor device side for transmission to and for further processing with a further control unit of a processing device comprising the two control units and designed to create a map of the surrounding area.

The technical teachings of the above protective rights are all guided by the idea of already carrying out the detection of an object in front of the vehicle with the aid of the ultrasonic sensor in the ultrasonic sensor and only then transmitting the object data after the objects have been detected. Here, however, synergy effects are lost when using several ultrasonic transmitters.

From the DE 10 2010 041 424 A1 a method for detecting an environment of a vehicle with a number of sensors is known in which at least one sensor during at least one echo cycle detects at least one piece of echo information about the environment and compresses it with an algorithm, and in which the at least one compressed Echo information is transmitted to at least one processing unit. Although the focuses DE 10 2010 041424 A1 also to the compressed transmission of detected object data, but it is already recognized that it makes general sense to transmit data extracted from the received echo signal (echo information) in compressed form to the control unit, without specifying a compression method becomes.

From the DE 10 2013 226 376 A1 a method for sensor connection is known. In section [0006] of the DE 10 2013 226 376 A1 explain the authors of DE 10 2013 226 376 A1 the state of the art. In claim 1 of DE 10 2013 226 376 A1 claim the authors of the DE 10 2013 226 376 A1 this self-proclaimed state of the art. From the DE 10 2013 226 376 A1 a method for operating a sensor system, in particular an ultrasonic sensor system, with an ultrasonic sensor and with a control unit is known. Data is transmitted from the ultrasonic sensor to the control unit in current-modulated form. Data is transmitted from the control device to the at least one ultrasonic sensor in a voltage-modulated manner.

From the DE 10 024 959 A1 a device for the unidirectional or bidirectional exchange of data between a measuring unit for measuring a physical quantity and a control/evaluation unit is known, which determines the physical quantity with a specified accuracy using the measurement data provided by the measuring unit, the measuring unit is equipped with the control / evaluation unit connected via a data line. The technical teaching of DE 10 024 959 A1 is based on the task of optimizing data transmission between a measuring unit and a remote control/evaluation unit. The object is achieved in that at least one compression unit is provided which is assigned to the measuring unit and which compresses the measurement data supplied by the measurement unit in a predetermined cycle in such a way that on the one hand the information content of the measurement data is completely or reduced to such an extent via the data line to the control / Evaluation unit is transmitted that the physical size is determined with the specified accuracy, and that on the other hand the amount of data transmitted is minimal.

From the DE 10 2013 015 402 A1 a method for operating a sensor device of a motor vehicle is known, in which by means of a sensor, in particular an ultrasonic sensor, the sensor device transmits a transmission signal into an area surrounding the motor vehicle and a signal component of the transmission signal (11) reflected on a target object in the surrounding area (7) is received as a raw echo signal (12). Data is transmitted via a data bus between the sensor and a control unit of the sensor device. A measurement variable for the target object is determined by means of the sensor device. The raw echo signal is converted into a raw digital echo signal by means of a converter of the sensor, and the raw digital echo signal is transmitted from the sensor via the data bus to the control unit, which determines the measured variable by relating the raw digital echo signal to a reference signal corresponding to the transmission signal. From the DE 10 2013 015 402 A1 . The DE 10 2013 015 402 A1 however, does not provide for decompression of the data in the control unit.

From the U.S. 2006/0 250 297 A1 a system for sensor fusion is known.

From the WO 2018 / 210 966 A1 and their priority European patent application with EPO file number EP17171320.9 the use of feature vectors is known. The WO 2018 / 210 966 A1 but leaves open how exactly the one classification takes place when there are several signal object features.

Task

The invention is therefore based on the object of creating a solution that solves the above task of reducing the bus bandwidth requirement for the transmission of measurement data from the ultrasonic sensor system to the computer system and possibly has other advantages.

This object is achieved by a sensor system according to claim 1. Further refinements are the subject of dependent claims.

Solution

As explained above, the technical teachings from the prior art are all guided by the idea of already carrying out the detection of an object in front of the vehicle with the aid of the ultrasonic sensor in the ultrasonic sensor and only then transmitting the object data after the objects have been detected. However, since synergy effects are lost when using several ultrasonic transmitters, it was recognized during the preparation of the disclosure presented here that it does not make sense to only transmit the echo data of the ultrasonic sensor itself, but rather all the data and only the data in a central computer system evaluate multiple sensors. For this purpose, however, the compression of the data for transmission via a data bus with a lower bus bandwidth must take place differently than in the prior art. In this way, synergy effects can then be developed. For example, it is conceivable that a vehicle has more than one ultrasonic sensor. In order to be able to distinguish between the two sensors, it makes sense if these two sensors send with different coding. In contrast to the prior art, however, both sensors should now detect the ultrasonic echoes of both emissions from both ultrasonic sensors and transmit them in suitably compressed form to the central computer system, where the ultrasonic reception signals are reconstructed. The objects are recognized only after the reconstruction (decompression). This also enables the fusion of the ultrasonic sensor data with other sensor systems such as radar, etc.

A method for transmitting sensor data from a sensor to a computer system is proposed. The method is particularly suitable for use in transmitting data from an ultrasonic reception signal from an ultrasonic sensor to a control unit as a computer system in a vehicle. The procedure is based on 1 explained. According to the proposed method, an ultrasonic burst is first generated and emitted into a free space, typically in the area surrounding the vehicle (step α of 1 ). An ultrasonic burst consists of several sound pulses that follow one another at an ultrasonic frequency. This ultrasonic burst is caused by a mechanical oscillator in an ultrasonic transmitter or ultrasonic transducer slowly oscillating and oscillating again. The ultrasonic burst thus emitted by the exemplary ultrasonic transducer is then reflected on objects in the vicinity of the vehicle and received by an ultrasonic receiver or the ultrasonic transducer itself as an ultrasonic signal and converted into an electrical reception signal (step β of 1 ). The ultrasound transmitter is particularly preferably identical to the ultrasound receiver and is then referred to below as a transducer. However, the principle explained below can also be used for separate receivers and transmitters. In the proposed ultrasonic sensor there is a signal processing unit which now analyzes and compresses the received electrical signal (step γ of 1 ) in order to minimize the necessary data transmission and to create free space for said status messages and further control commands from the control computer to the signal processing unit or the ultrasonic sensor system.

The compressed electrical reception signal is then transmitted to the computer system (step δ of 1 ).

The associated method is thus used to transmit sensor data, in particular an ultrasonic sensor, from a sensor to a computer system, in particular in a vehicle. It begins by emitting an ultrasonic burst (step α of 1 ). An ultrasonic signal is received and an electrical reception signal is formed (step β of 1 ) and carrying out a data compression of the received signal (step γ of 1 ) to generate compressed data (step γ of 1 ) and the detection of at least two or three or more predetermined properties. The electrical reception signal is preferably obtained by sampling (step ya of 2 ) into a sampled received signal consisting of a time-discrete stream of samples. Each sample can typically be assigned a sampling time as a time stamp of this sample. The compression can be carried out, for example, by a wavelet transformation (step yb of 2 ) take place. For this purpose, the received ultrasonic signal in the form of the sampled received signal can be compared with predetermined signal basic forms, which are stored in a library, for example, by forming a correlation integral (see also Wikipedia on this term) between the predetermined signal basic forms and the sampled received signal. The predetermined signal basic forms are also referred to below as signal object classes. By forming the correlation integral, spectral values associated with each of these prototypical signal object classes are determined for each of these prototypical signal object classes. Since this happens continuously, the spectral values themselves represent a stream of time-discrete instantaneous spectral values, with each spectral value being able to be assigned a time stamp. An alternative but mathematically equivalent method is the use of optimal filters (English matched filter) for each predetermined signal object class (signal basic form). Since, as a rule, several prototypical signal object classes are used, which can also be subjected to different time spreads (see also "wavelet analysis"), this typically results in a time-discrete stream of multidimensional vectors of spectral values of different prototypical signal object classes and their different respective temporal ones spreads. A time stamp is assigned to each of these multidimensional vectors. Each of these multidimensional vectors is a so-called feature vector. It is therefore a time-discrete stream of feature vectors. A time stamp is preferably assigned to each of these feature vectors. (Step yb der 2 )

Due to the ongoing time shift, there is also a time dimension. In this way, the feature vector of the spectral values can also be supplemented with past values or values that depend on them, for example time integrals or derivatives or filter values of one or more of these values, etc. This can further increase the dimension of these feature vectors within the feature vector data stream. In order to keep the effort small in the following, the limitation to a few prototypical signal object classes during the extraction of the feature vectors from the scanned input signal of the ultrasonic sensor makes sense. Thus, for example, optimal filters (English matched filter) can then be used in order to continuously monitor the occurrence of these prototypical signal object classes in the received signal.

The isosceles triangle and the double peak, for example, can be specifically named here as particularly simple prototypical signal object classes. A prototypical signal object class usually consists of a specified spectral coefficient vector, ie a specified prototypical feature vector value.

To determine the relevance of the spectral coefficients of a feature vector of an ultrasonic echo signal, the amount of a distance between these properties, the elements of the vector of the instantaneous spectral coefficients (feature vector), is determined from at least one prototypical combination of these properties (prototype) in the form of a prototypical signal object class, which is symbolized by a predefined prototypical feature vector (prototype or prototype vector) from a library of predefined prototypical signal object class vectors (step yd of 2 ). The spectral coefficients of the feature vector are preferably normalized before correlation with the prototypes (step yc of 2 ). The distance determined in this distance determination can, for example, be the sum of all differences between a respective spectral coefficient of the specified prototypical feature vector (prototype or prototype vector) of the respective prototype and the corresponding normalized spectral coefficients of the current feature vector of the ultrasonic echo signal. A Euclidean distance would be formed by the square root of the sum of the squares of all differences between each spectral coefficient of the given prototypical feature vector (prototypes or prototype vector) of the prototype and the corresponding normalized spectral coefficient of the current feature vector of the ultrasonic echo signal. However, this spacing formation is generally too complex. Other methods of spacing are conceivable. A symbol and possibly also a parameter, for example the distance value and/or the amplitude before the normalization, can then be assigned to each predefined prototypical feature vector (prototype or prototype vector). If the distance determined in this way falls below a first threshold value and if it is the smallest distance between the current feature vector value and one of the predefined prototypical feature vector values (prototypes or values of the prototype vectors), then its symbol is used as a recognized prototype. This results in a pairing of recognized prototypes and time stamp of the current feature vector. The data is then preferably transmitted (step δ of 2 ), here the determined symbol that best symbolizes the recognized prototype and, for example, the distance and the time of occurrence (time stamp) to the computer system only if the amount of this distance is below the first threshold value and the recognized prototype is a to be transmitted prototype is. It can be the case that prototypes that cannot be recognized, for example for noise, for example the absence of reflections, etc., are also stored. This data is irrelevant for obstacle detection and should therefore not be transmitted. A prototype is therefore recognized when the amount of the determined distance between the current feature vector value and the specified prototypical feature vector value (prototype or value of the prototype vector) is below this first threshold value (step ye der 2 ). It is therefore no longer the ultrasonic echo signal itself that is transmitted, but only a sequence of symbols for recognized typical temporal signal curves and time stamps associated with these signal curves in a specific time segment (step δ of the 2 ). It is then preferred for each recognized signal object only one symbol for the recognized signal form prototype, its parameters (e.g. amplitude of the envelope (of the ultrasonic echo signal) and/or time stretching) and a temporal reference point of the occurrence of this signal form prototype (the time stamp) as recognized signal object transmitted. The transmission of the individual sampled values or times at which threshold values are exceeded by the envelope of the sampled received signal (the ultrasonic echo signal), etc., is omitted. In this way, this selection of the relevant prototypes leads to massive data compression and a reduction in the required bus bandwidth.

A quantitative detection of the presence of a combination of properties takes place with the formation of an estimated value - here e.g. the inverse distance between the representative of the prototypical signal object class in the form of the specified prototypical feature vector (prototype or prototype vector) - and the subsequent transmission of the compressed data to the computer system if the amount of this estimated value (e.g. inverse distance) is above a second threshold or the inverse estimated value is below a first threshold. The signal processing unit of the ultrasonic sensor thus performs data compression of the received signal to generate compressed data.

For better clarity, the determination of the distance should be explained again here.

This determination of distance is also known as classification from the signal processing of statistical signals. Examples of classifiers are logistic regression, the cuboid classifier, the distance classifier, the nearest neighbor classifier, the polynomial classifier, the cluster method, artificial neural networks, and latent class analysis.

A classifier example is in 7 shown.

An exemplary physical interface (101) controls an exemplary ultrasonic transducer (100) and causes it to emit an exemplary ultrasonic transmission pulse or ultrasonic transmission burst. The ultrasonic transducer (100) receives the from a not in 7 drawn object reflected exemplary ultrasonic transmission pulse or ultrasonic transmission burst with an amplitude change, a delay and typically with a distortion caused by the nature of the reflecting object. In addition, there are typically several objects in the area surrounding the vehicle that contribute to modifying the reflected ultrasonic wave. The ultrasonic transducer (100) converts the received reflected ultrasonic wave into an ultrasonic transducer signal (102). The physical interface converts this ultrasonic transducer signal, typically by filtering and/or amplification, into an ultrasonic echo signal (1). The feature vector extractor (111) extracts from this ultrasonic echo signal (1). The physical interface (101) preferably transmits the ultrasonic echo signal (1) to the feature vector extractor (111) as a discrete-time received signal consisting of a sequence of sampled values begins. In this case, a time date (time stamp) is preferably assigned to each sampled value. The feature vector extractor (111) has in the example of 7 a plurality of m (where m is a positive integer) matched filters (matched filter 1 to matched filter m). These are used to determine m intermediate parameter signals (123), each relating to the presence of a signal base object preferably assigned to the respective intermediate parameter signal, preferably with the aid of a suitable filter (e.g. an optimal filter) from the sequence of sampled values of the ultrasonic echo signal (1). The resulting intermediate parameter signals (123) are also in the form of a time-discrete sequence of respective intermediate parameter signal values, which are preferably each correlated with a date (time stamp). Precisely one time date (time stamp) is thus preferably assigned to each intermediate parameter signal value.

A subsequent significance increaser (125) carries out a matrix multiplication of the vector of the intermediate parameter signal values of the intermediate parameter signal (123) with a so-called LDA matrix (126). This is typically determined at the time of construction using statistical methods of statistical signal processing and pattern recognition. The significance enhancer thus generates the feature vector signal (138). In doing so, it maps the m intermediate parameter signal values to n (where n is an integer) parameter signal values of the feature vector signal (138). The feature vector signal (138) thus typically comprises n parameter signals. n<m is preferred. At this point it should only be noted that the term “feature vector” is also often referred to as feature vector in statistical signal theory and in pattern recognition. The feature vector signal (138) is thus designed, among other things, as a time-discrete sequence of feature vector signal values each with n parameter signal values of the preferably n parameter signals of the feature vector signal (138), which have the parameter signal values and further parameter signal values with the same time date ( timestamp) included. Here n is the dimension of the individual feature vector signal values, which are preferably equal from one feature vector value to the next feature vector value. In this sense, a feature vector signal value is a vector with a time stamp that includes a plurality of, preferably n, parameter signal values. This respective time data (time stamp) is assigned to each feature vector signal value formed in this way.

The evaluation of the time profile of the feature vector signal (138) in the resulting n-dimensional phase space now follows, as well as the inference of a recognized signal base object by determining an evaluation value (e.g. the distance).

For this purpose, a distance determiner (or classifier) (112) compares the current feature vector signal value of the feature vector signal (138) with a plurality of prototype feature vector signal value prototypes previously stored in a prototype database (115). This is explained in more detail below. The distance finder (or classifier) (112) determines an evaluation value for each of the examined feature vector signal value prototypes in the prototype database (115), which indicates the extent to which the respective feature vector signal value prototype in the prototype database (115) corresponds to the current Feature vector signal value equals. This evaluation value is hereinafter referred to as distance. The feature vector signal value prototypes are preferably each assigned to exactly one signal basic object. The feature vector signal value prototype of the prototype database (115) with the smallest distance to the current feature vector signal value then matches them best. If the distance is less than a predetermined threshold value, then this feature vector signal value prototype of the prototype database (115) represents the signal base object (121) recognized with the highest probability.

This recognition process is carried out several times in succession, so that a determined signal basic object sequence results from the chronological sequence of the recognized signal basic objects (121).

The probable signal object (122) is then determined by determining that sequence of predetermined signal basic object sequences in a signal object database (116) which is most similar to the determined signal basic object sequence. As explained above, a signal object consists of a chronological sequence of basic signal objects. A symbol in the signal object database (116) is typically assigned to the signal object in a predefined manner.

For example, this estimation of the signal basic object sequence can be done by a Viterbi estimator (113). In the simplest case, the number of signal basic objects recognized within a specified period of time, which, according to their position in the sequence of recognized signal basic objects, correspond to the position of an expected signal basic object in an expected sequence of signal basic objects specified as a specified signal object in the signal object database (116) minus the number of the basic signal objects detected within the specified period of time, which, according to their position in the sequence of basic signal objects detected, correspond to the position of an expected basic signal object in an in the signal object database (116) does NOT match the expected sequence of signal basic objects specified as the specified signal object, is taken as the evaluation value for the match. In this way, the Viterbi estimator determines an evaluation value for each of the predefined signal objects in the signal object database (116), the signal objects in the signal object database (116) consisting of predefined sequences of expected basic signal objects and being correlated with a respective symbol.

Figuratively speaking, it is checked here whether the point to which the n-dimensional feature vector signal (138) points in the n-dimensional phase space, on its way through the n-dimensional phase space in a predetermined time sequence, follows predetermined points in this n -dimensional phase space closer than a predetermined maximum distance approaches. The feature vector signal (138) therefore has a time profile. An evaluation value (e.g. a distance) is then calculated, which can, for example, reflect the probability of the existence of a specific sequence. This evaluation value, which is again assigned a time datum (time stamp), is then preferably compared within the Viterbi estimator (113) with a threshold value vector, forming a Boolean result that can have a first and a second value. If this Boolean result has the first value for this date (time stamp) in time, the symbol of the signal object and the date (time stamp) assigned to this symbol are transmitted from the sensor to the computer system. As a result, the recognized signal object (122) is preferably transmitted with its parameters. If necessary, further parameters can be transmitted depending on the detected signal object.

At this point we want to go into the processing of the feature vector signal (138) again for better clarity. The ultrasonic echo signal (1) is preferably a sequence of quantization vectors—the ultrasonic echo signal values—whose components, the measured parameters, will generally not be completely independent of one another. As a rule, each ultrasonic echo signal value of an ultrasonic echo signal (1) has insufficient selectivity for the precise detection of a signal object in complex contexts.

This is precisely the deficiency in the prior art. By creating one or more such quantization vectors from the continuous stream of analog physical values of the ultrasonic echo signal (1) at typically regular or regular time intervals through the physical interface (101) (see 7 ) the multidimensional ultrasonic echo signal value data stream, quantized in terms of time and value, is produced in the form of the ultrasonic echo signal (1).

In a first exemplary processing step, the multidimensional ultrasonic echo signal (1) obtained in this way in the form of one or more streams of quantization vectors is first divided into individual frames of a defined length, filtered, normalized, then orthogonalized and, if necessary, by a non-linear mapping - e.g. logarithmization and cepstrum analysis - suitably biased. This is due to the block of matched filters in the feature vector extractor (111). 7 implied. Instead of the block of the optimal filter block, other signal processing structures for generating the intermediate parameter signals (123) can also be imagined here. For example, derivations of the ultrasonic echo signal values generated in this way can also be formed here. Finally, the significance of the determined intermediate parameter signal (123) increases in a significance increase unit (125) to form the actual feature vector signal (138). As described, this can be done, for example, by multiplying the multidimensional quantification sector by a so-called predetermined LDA matrix (126).

1. a) durch ein Neuronales Netz oder
2. b) durch einen HMM-Erkenner
3. c) durch ein Petri-Netz

The following step of recognition in the distance detector (or classifier) (112) can be carried out, for example, using two different methods:

1. a) by a neural network or
2. b) by an HMM recognizer
3. c) by a Petri net

• Mit Hilfe der besagten vordefinierten LDA-Matrix (126) wird der Zwischenparameterdatenstrom (123) so vom mehrdimensionalen Eingangsparameterraum auf einen neuen Parameterraum durch den Signifikanzsteigerer (125) abgebildet, wodurch seine Selektivität maximal wird. Die Komponenten der dabei gewonnenen neuen transformierten Merkmalsvektoren werden hierbei nicht nach realen physikalischen oder sonstigen Parametern, sondern nach maximaler Signifikanz ausgewählt, was besagte maximale Selektivität zur Folge hat.

The HMM recognizer ( 7 ) described:

• With the help of said predefined LDA matrix (126), the intermediate parameter data stream (123) is thus mapped from the multidimensional input parameter space to a new parameter space by the significance increaser (125), whereby its selectivity becomes maximum. The components of the newly transformed feature vectors obtained in this way are not selected according to real physical or other parameters, but according to maximum significance, which results in said maximum selectivity.

The LDA matrix (126) is usually previously calculated offline at the time of construction on the basis of example data streams with known signal object data sets, ie data sets that were obtained with specified structures of the signal curve, beforehand by means of a training step.

If care is taken that all elements of the method carried out by the distance detector (or the classifier) (112) execute at least locally reversible functions, deviations in the signal curve can be taken into account in the form of an approximately linear transformation function.

The prototypes from sample data streams for the specified prototypical signal curves (prototypical signal basic objects) in the coordinates of the new parameter space are calculated in the construction phase and stored in a prototype database (115) for later recognition. In addition to this statistical data, this can also contain instructions for a computer system as to what should happen in the event of successful or failed recognition of the respective basic signal object prototype. As a rule, the computer system will be the computer system of the sensor system.

In this Prototype database (115) saved as signal primitive object prototypes.

1. 1. Entspricht der erkannte Feature-Vektor-Wert des Feature-Vektorsignals (138) einem der vorgespeicherten Signalgrundobjektprototypen der Prototypendatenbank (115) oder nicht und mit welcher Wahrscheinlichkeit und Zuverlässigkeit?
2. 2. Wenn es einer der bereits gespeicherten Signalgrundobjektprototypen der Prototypendatenbank (115) ist, welcher ist es und mit welcher Wahrscheinlichkeit und Zuverlässigkeit?

In later operation, the feature vector signal values of the feature vector signal (138) are now compared with these previously stored, i.e. learned signal basic object prototypes (115), for example by calculating the Euclidean distance between a quantization vector in the coordinates of the new parameter space and all of these previously stored signal basic object prototypes (115) compared in the distance finder (112). At least two detections are made here:

1. 1. Does the detected feature vector value of the feature vector signal (138) correspond to one of the pre-stored signal primitive prototypes of the prototype database (115) or not and with what probability and reliability?
2. 2. If it is one of the already stored signal primitive prototypes of the prototype database (115), which one is it and with what probability and reliability?

In order to provide the first recognition, dummy prototypes are usually also stored in the prototype database (115) of the basic signal object prototypes, which should largely cover all parasitic parameter combinations occurring during operation. Said basic signal object prototypes are stored in the prototype database (115).

For the basic signal object prototypes in the prototype database (115), the distance can be determined at the time of construction according to the method used in the distance determiner (112) for each pairing of two different basic signal object prototypes in the prototype database (115). This then results in a minimum prototype distance. This is stored in the prototype database (115) or in the distance determiner, preferably also halved as half the minimum prototype distance.

For example, if the distance between the current feature vector signal value of the feature vector signal (138) and a signal basic object prototype in the prototype database (115) determined by the distance determination (112) falls below this minimum half prototype distance, this signal basic object prototype is evaluated as recognized. From this point in time it can be ruled out that further distances to other basic signal object prototypes in the prototype database (115) calculated in the course of a continued search can supply even smaller distances. The search can then be aborted, which halves the time on average and thus saves the resources of the sensor.

The minimum Euclidean distance can be calculated using the following formula, for example: $$Dis{t}_{fV\_CbE}=\underset{Cb\_cnt=Cb\_an\mathrm{e.g}}{\overset{1}{Min}}\left[{\left({\displaystyle \sum _{\text{dim\_}cnt=\text{dim}}^{1}f{V}_{\text{dim\_}cnt}-C{b}_{Cb\_cnt,\mathrm{i.e}in\_cnt}}\right)}^2\right]$$

Here, dim_cnt stands for the dimension index that runs up to the maximum dimension of the feature vector (138) dim.

FV _{dim_cnt} represents the parameter value of the feature vector signal value of the feature vector (138) corresponding to index dim_cnt.

Cb_cnt represents the number of the signal primitive object prototype in the prototype database (115).

Accordingly, Cb _{CB_cnt,dim_cnt} stands for the parameter value corresponding to dim_cnt of the entry of the signal basic object prototype in the prototype database (115) which is associated with the signal basic object prototype corresponding to Cb_cnt.

Dist _{FV_CbE} stands for the minimum Euclidean distance obtained, which is exemplary here. When searching for the smallest Euclidean distance, the number Cb_cnt that produces the smallest distance is noted.

An example assembler code is given for clarification:

The degree of confidence for correct recognition is derived from the scatter of the basic data streams taken as a basis for a signal basic object prototype and the distance of the current feature vector value of the feature vector signal (138) from its center of gravity.

8th demonstrates different detection cases. For simplification, the representation for a two-dimensional feature vector signal is selected, in which each feature vector signal value includes a first parameter value and a second parameter value. This is only used here to be able to better present the methodology on a two-dimensional piece of paper. In reality, the feature vector signal values of the feature vector signal (138) are typically always multidimensional.

Focal points of various prototypes (141, 142, 143, 144) are marked. As described above, half the minimum distance between these basic signal object prototypes can now be stored in the prototype database (115) in the prototype database (115). This would then be a global parameter that would be equally valid for all signal primitive object prototypes in the prototype database (115). This decision is made using this minimum distance. However, it presupposes that the scattering of the signal basic object prototypes, which are marked by their centers of gravity (141, 142, 143, 144), are more or less identical. If the distance determination (or other evaluation) by the distance determiner (112) (or classifier) is optimal, then this is also the case. This would correspond to a circle around the centroid (141, 142, 142, 144) of each of the signal primitive prototypes of the prototype database (115).

In reality, however, this can only rarely be achieved. An improvement in the recognition performance can therefore be achieved if the spread for the signal basic object prototypes in the prototype database (115) were also stored in each case. This would correspond to a circle around each of the signal primitive prototypes of the prototype database (115) with a radius specific to the signal primitive prototype. The downside is an increase in computing power.

A further improvement in the recognition performance can be achieved if the spread for the signal basic object prototype of the prototype database (115) is modeled by an ellipse. For this purpose, instead of the radius as before, the main axis diameter of the scattering ellipse and its tilting relative to the coordinate system in the prototype database (115) for preferably each of the signal basic object prototypes in the prototype database (115) must be stored. The disadvantage is another massive increase in computing power and memory requirements.

Of course, the calculation can be further complicated, but this usually only massively increases the effort and no longer significantly increases the recognition performance for the basic signal object prototypes of the prototype database (115).

It is therefore recommended here to use the simplest of the options described.

The position of the by the distance determination (112) in the exemplary two-dimensional parameter space 8th determined current feature vector signal values of the feature vector signal (138) can now be extremely different. So it is conceivable that such a first such exemplary feature vector signal value (146) is too far away from the centroid coordinates (141, 142, 143, 144) of the centroid of any signal basic object prototype of the prototype database (115). This distance threshold value can be, for example, the said minimum half prototype distance. It is also possible that the scattering areas of the signal basic object prototypes overlap around their respective focal points (143, 142) and a second exemplary determined current feature vector signal value (145) of the feature vector signal (138) lies in the overlapping area. In this case, a hypothesis list can contain both signal primitive object prototypes with different probabilities as an attached parameter because of different distances. A basic signal object is then not transferred to the Viterbi estimator (113) as the most probable one, but rather a vector of basic signal objects that may be present is transferred to the Viterbi estimator (113). From the chronological sequence of these hypothesis lists, the Viterbi estimator then searches for the possible sequence that has the greatest probability of one of the predetermined signal basic object sequences in its signal object database compared to all possible paths through the signal basic objects (121) recognized as possible by the distance detector (112). hypotheses lists received from the Viterbi estimator (113). In such a path, exactly one recognized signal basic object prototype of this hypothesis list must be run through this path for each hypothesis list.

In the best case, the current feature vector signal value (148) is in the scatter range (threshold ellipsoid) (147) around the center of gravity (141) of a single signal basic object prototype (141) which is thus safe is recognized by the distance detector (112) and forwarded to the Viterbi estimator (113) as a recognized basic signal object (121).

It is conceivable, for improved modeling of the scattering range of a single signal basic object prototype, to model this by means of a plurality of signal basic object prototypes, which are circular in this case, with associated scattering ranges. It is therefore possible for a number of signal basic object prototypes in the prototype database (115) to represent the same signal basic object prototype in the understanding of a signal basic object class. The danger here is that due to the distribution of the probability of a signal basic object prototype to several such sub-signal basic object prototypes, the probability of the individual sub-signal basic object prototype can become smaller than that of another signal basic object prototype whose probability was lower than that of the original signal basic object prototype. Thus, this other signal primitive prototype can possibly falsely assert itself.

• Ein entscheidender Punkt ist, dass der Rechenaufwand mit Cb_anz * dim steigt.

Furthermore, the computing power that must be made available in order to reliably recognize the signal basic object prototypes in the prototype database (115) represents a major problem. This should be discussed a little further:

• A crucial point is that the computational effort increases with Cb_anz * dim.

For a non-optimized HMM recognizer, the number of assembler instructions that must be executed to compute a vector component is approximately 8 steps.

The number A_Abst of assembler steps necessary to calculate the distance from a single signal primitive object prototype (CbE) of the prototype database to a single feature vector signal value (FV) is calculated approximately as follows: $$\text{A\_dist}=\text{FV\_Dimension*8}+\text{8th}$$

This leads to the number A_CB of assembler steps for determining the signal primitive object prototype of the prototype database (115) with the smallest distance: $$\text{A\_CB}=\text{Cb\_anz*}\left(\text{A\_dist}\right)+4=\text{Cb\_anz}*\left(\text{FV\_Dimension*8}+\text{8th}\right)+4$$

Using the example of a medium HMM recognizer with 50000 signal basic object prototypes (number of signal basic object prototype entries in the prototype database=CB_anz) and 24 FV_Dimensions (number of parameter values in a feature vector signal value=feature vector dimension= FV_Dimension), the number of steps is : $$\begin{array}{c}50000*\left(24*\mathrm{8th}+\mathrm{8th}\right)+4^{\sim }10\text{Million operations per feature}-\text{Vector signal value of the feature}-\\ \text{vector signal}\left(138\right)\end{array}$$

With a relatively low sampling rate of 8kHz = 8000 FV per second (feature vector signal values of the feature vector signal (124) per second), a computing power of 8GIpS (8 billion instructions per second) is required.

This is unacceptable given the challenges of energy saving in electromobility and/or reducing the CO2 footprint.

If an optimized HMM detection method is carried out by the distance detector (112) or the classifier (112), as already mentioned, the smallest distance between two signal basic object prototypes in the prototype database (115) is precalculated and stored in the prototype database (115) or in of the distance calculation (112). This has the advantage that the search can then be aborted by the distance finder (112) when a distance between a current feature vector signal value of the feature vector signal (138) and a signal basic object prototype of the prototype database (115) by the distance finder (115) was found that is less than half this smallest distance. This halves the average search time for the distance finder (112). Further optimizations can be made if the prototype database (115) is sorted according to the statistical occurrence of the signal basic object prototypes in real ultrasonic echo signals (1). In this way it can be ensured that the most frequent signal basic object prototypes are found much more quickly be, which further shortens the computing time of the distance detector (112) or classifier (112) and further reduces the power consumption.

• Wieder sind es 8 Schritte zur Berechnung des Abstands einer Vektor-Komponente. Die Schritte zur Berechnung des Abstands A_Abst eines Signalgrundobjektprototyp-Eintrags (CbE) in der Prototypendatenbank (115) zum aktuellen Feature-Vektorsignalwert (FV) des Feature-Vektorsignals (138) sind wieder: $$\text{A\_Abst}=\text{FV\_Dimension*8}+\text{8}$$
  

For a distance finder that runs such an optimized HMM detection process, the computing power requirement is now as follows:

• Again, there are 8 steps to calculate the distance of a vector component. The steps for calculating the distance A_Abst of a signal primitive object prototype entry (CbE) in the prototype database (115) to the current feature vector signal value (FV) of the feature vector signal (138) are again: $$\text{A\_dist}=\text{FV\_Dimension*8}+\text{8th}$$
  

The number of steps for finding the signal basic object prototype entry of the prototype database (115) with the smallest distance A_CB with optimization is a little higher: $$\text{A\_CB}=\text{Cb\_anz*}\left(\text{A\_dist}\right)+4=\text{Cb\_anz}*\left(\text{FV\_Dimension*8}+10\right)+4$$

The two additional assembler commands are necessary to check whether the determined distance between the current feature vector signal value and the currently examined signal basic object prototype of the prototype database (115) is below half the smallest distance between the signal basic object prototypes of the prototype database (115).

Furthermore, the number CB_Anz of signal basic object prototype entries in the prototype database (115) for mobile and energy self-sufficient applications is limited to 4000 prototype database entries of signal basic object prototypes in the prototype database (115) or even less.

In addition, the number of feature vector signal values per second within the feature vector signal (138) is reduced by filtering in the feature vector extractor (111) and lowering the sampling rate in the feature vector extractor (111).

• Der besagte Abstandsermittler (112) oder Klassifikator (112), der ein mittleres HMM-Erkennungsverfahren ausführt, wird nun mit einer Prototypendatenbank (115) mit nur noch einem knappen Zehntel der Einträge z.B. mit 4000 Einträgen (CbE) und weiterhin mit 24 Feature-Vektor-Signal-Dimensionen (also 24 Parametersignalen) betrieben.

This is explained using a simple example:

• Said distance finder (112) or classifier (112), which executes a medium HMM recognition method, is now being used with a prototype database (115) with only a tenth of the entries, e.g. with 4000 entries (CbE) and still with 24 feature vectors -Signal dimensions (i.e. 24 parameter signals).

The number of steps is now $$\begin{array}{c}4000*\left(24*\mathrm{8th}+10\right)+4^{\sim }\underset{\_}{808004}\text{operations per feature}-\text{Vector signal value of the feature}-\text{vector signal}\\ \left(138\right)\end{array}$$

When the feature vector signal value rate is reduced to 100 feature vector signal values per second, extracted from a 10 ms time window in the feature vector extractor (111) over, for example, 80 samples each and the search is terminated when the distance of the current feature vector signal value to the processed signal basic object prototype of the prototype database (115) is less than half the smallest prototype database entry distance, the effort is at least halved if the prototype database (115) is sorted appropriately.

The required computing power then drops to <33DSP-Mips (33 million operations per second). In reality, the prototype database sorting leads to even lower computing power requirements of, for example, 30Mips. This means that the system is real-time capable and can only be integrated into a single IC and thus into a sensor.

The search area can be restricted by pre-selection. A prerequisite is a uniform distribution of the data = focal points of the quadrants in the geometric quadrant center.

The need to reduce the size of the prototype database (115) has advantages and disadvantages:

A reduction in the number of entries in the prototype database (115) increases both the false acceptance rate (FAR), ie the false signal basic object prototypes that are used as signal basic object prototypes are accepted, as well as the false rejection rate (FRR), i.e. the actually existing signal basic object prototypes that are not recognized.

On the other hand, this reduces the resource requirements (computing power, chip areas, memory, power consumption, etc.).

In addition, the previous history, i.e. the previously recognized signal basic object prototypes, can be used by the distance determiner (112) when forming hypotheses. A suitable model for this is the so-called Hidden Markov Model (HMM).

A confidence level and a distance to the measured current feature vector signal value of the feature vector signal (138) can thus be derived for each signal basic object prototype, which can also be further processed by the Viterbi estimator (113). It also makes sense to output a list of hypotheses for each of the detected basic signal object prototypes (121) which contains, for example, the ten most probable basic signal object prototypes with the respective probability and reliability of detection.

Since not every temporal and spatial signal basic object sequence can be assigned to a signal object and is therefore meaningful, it is possible to evaluate the chronological succession of the hypothesis lists of the consecutive frames using a Viterbi estimator (113).

Here the signal basic object prototype sequence path through the consecutive hypothesis lists is to be found which has the highest probability and is present in the signal object database (116) of the Viterbi estimator (113).

1. 1. Ist die wahrscheinlichste Folge von Signalgrundobjektprototypen eine der bereits eingespeicherten Folge von Signalgrundobjektprototypen oder nicht und mit welcher Wahrscheinlichkeit und Zuverlässigkeit?
2. 2. Wenn sie eine der bereits gespeicherten Folge von Signalgrundobjektprototypen ist, welche ist es und mit welcher Wahrscheinlichkeit und Zuverlässigkeit?

Again, at least two recognitions are made:

1. 1. Is the most probable sequence of signal basic object prototypes one of the already stored sequence of signal basic object prototypes or not and with what probability and reliability?
2. 2. If it is one of the sequence of signal primitive prototypes already stored, which one is it and with what probability and reliability?

For this purpose, on the one hand, such a prototype can be entered into a signal object database (116) in the form of an entry consisting of a predetermined sequence of signal basic object prototypes using a training program, on the other hand, this can also be done manually using a type-in tool, which allows the entry of these sequences of Signal primitive object prototyping enabled via a keyboard.

Using the Viterbi estimator (113), the most probable of the predefined sequences of signal basic object prototypes for a sequence of recognized signal basic object prototypes (121) can be determined from the sequence of the hypothesis lists of the distance determiner (112) or the classifier (112). This also applies in particular if individual basic signal object prototypes were incorrectly recognized by the distance detector (112) or the classifier (112) due to measurement errors. In this respect, it makes a lot of sense for the Viterbi estimator (113) to take over sequences of signal basic object prototype hypothesis lists from the distance determiner (112) or classifier (112), as described above. The result is the signal object (122) recognized as the most probable or, analogously to the previously described emission calculation of the distance estimator (112) or classifier (112), a signal object hypothesis list.

Finally, we consider the functional components of the signal object recognition engine. This is in 7 entered as Viterbi estimator (113). This Viterbi estimator (113) search accesses the signal object database (116). The signal object database (116) is fed on the one hand by a training tool and on the other hand by a tool in which these sequences of signal basic object prototypes can be defined by textual input. The possibility of downloading in production is only mentioned here for the sake of completeness.

The basis of the sequence recognition for the chronological order of the signal basic object prototypes in the Viterbi estimator (119) is, for example, a hidden Markov model. The model is made up of different states. in the in 9 given example, these states are symbolized by numbered circles. In the example in question 9 the circles are numbered from Z1 to Z6. There are transitions between the states. These transitions are in the 9 with the letter a and two indices i, j designated. The first index i designates the number of the source node, the second index j the number of the destination node. In addition to the transitions between two different nodes, there are also transitions a _ii or a _jj that lead back to the start node. In addition, there are transitions that make it possible to skip nodes. From the sequence, there is a probability of actually observing a k-th observable b _k . This results in sequences of observables that can be observed with probabilities _bk that can be calculated in advance.

It is important that every Hidden Markov Model consists of unobservable states q ⁱ . The transition probability a _ij exists between two states q ⁱ and q ^j .

Thus the probability p for the transition from q ⁱ to q ^j can be written as: $$p\left(q_n^j|q_{n-1}^i\right)\equiv a_{ij}$$

n stands for a discrete point in time. The transition thus takes place between step n with state q ⁱ and step n+1 with state q ^j .

The emission distribution b _i (Ge) depends on the state q ⁱ . As already explained, this is the probability of observing the elementary gesture Ge (the observable) when the system (Hidden Markov Model) is in state q ⁱ : $$p\left(Ge|q^i\right)\equiv b_i\left(Ge\right)$$

In order to be able to start the system, the initial states must be defined. This is done by a probability vector π _i . Then it can be stated that a state q' with probability π _i is an initial state: $$p\left(q^i{}_1\right)\equiv {\pi }_i$$

bestimmt werdenImportantly, a new model must be created for each sequence of signal primitive object prototypes. In a model M, the observation probability for a temporal observation sequence of signal basic object prototypes $$G\stackrel{\mathrm{\rho }}{e}=\left(\mathrm{<figure-callout\; id="1"\; label="G\; e"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">G</figure-callout>}{e}_{}{}_1,\mathrm{<figure-callout\; id="2"\; label="G\; e"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0030.png"\; state="\{\{state\}\}">G</figure-callout>}{e}_{}{}_2,...G{e}_N\right)$$

to be determined

This corresponds to a temporal sequence of states that cannot be observed directly, which corresponds to the following sequence: $$\stackrel{\mathrm{\rho }}{Q}=\left({\mathrm{<figure-callout\; id="1"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">q</figure-callout>}}_1,{\mathrm{<figure-callout\; id="2"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0030.png"\; state="\{\{state\}\}">q</figure-callout>}}_2,...q_N\right)$$

The probability p, dependent on the model M, the temporal sequence of states Q and the temporal observation sequence Ge, of observing this sequence of states Q is: $$\begin{array}{l}p\left(G\stackrel{\mathrm{\rho }}{e}|\stackrel{\mathrm{\rho }}{Q},M\right)=p\left(\mathrm{<figure-callout\; id="1"\; label="G\; e"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">G</figure-callout>}{e}_{}{}_1,\mathrm{<figure-callout\; id="2"\; label="G\; e"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0030.png"\; state="\{\{state\}\}">G</figure-callout>}{e}_{}{}_2,...G{e}_N|{\mathrm{<figure-callout\; id="1"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">q</figure-callout>}}_1,{\mathrm{<figure-callout\; id="2"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0030.png"\; state="\{\{state\}\}">q</figure-callout>}}_2,...q_N\right)\\ =p\left(\mathrm{<figure-callout\; id="1"\; label="G\; e"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">G</figure-callout>}{e}_{}{}_1|q_1\right)\cdot p\left(\mathrm{<figure-callout\; id="2"\; label="G\; e"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0030.png"\; state="\{\{state\}\}">G</figure-callout>}{e}_{}{}_2|q_2\right)\cdot ......p\left(G{e}_N|q_N\right)\\ ={\displaystyle \prod _{n=1}^{N}p\left(G{e}_n|q_n\right)}\\ ={\displaystyle \prod _{n=1}^N{b}_n\left(G{e}_n\right)}\end{array}$$

im Modell M: $$\begin{array}{l}p\left(\stackrel{\mathrm{\rho }}{Q}|M\right)=p\left(q_1,q_2,\dots q_N|M\right)\\ =p\left(q_1\right)\cdot p\left(q_2|q_1\right)\cdot p\left(q_3|q_1,q_2\right)\cdot \dots \dots p\left(q_N|q_1,q_2,\dots q_{N-1}\right)\\ =p\left(q_1\right){\displaystyle \prod _{n=2}^{N}p\left(q_n|q_{n-1}\right)}\\ ={\pi }_1{\displaystyle \prod _{n=2}^N{a}_{\left(n-1\right)n}}\end{array}$$

This results in the probability of a sequence of states $\stackrel{\mathrm{\rho }}{Q}=\left({\mathrm{<figure-callout\; id="1"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">q</figure-callout>}}_1,{\mathrm{<figure-callout\; id="2"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0030.png"\; state="\{\{state\}\}">q</figure-callout>}}_2,...q_N\right)$

in model M: $$\begin{array}{l}p\left(\stackrel{\mathrm{\rho }}{Q}|M\right)=p\left({\mathrm{<figure-callout\; id="1"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">q</figure-callout>}}_1,{\mathrm{<figure-callout\; id="2"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0030.png"\; state="\{\{state\}\}">q</figure-callout>}}_2,...q_N|M\right)\\ =p\left(q_1\right)\cdot p\left({\mathrm{<figure-callout\; id="2"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0030.png"\; state="\{\{state\}\}">q</figure-callout>}}_2|q_1\right)\cdot p\left({\mathrm{<figure-callout\; id="3"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0038.png"\; state="\{\{state\}\}">q</figure-callout>}}_3|{\mathrm{<figure-callout\; id="1"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">q</figure-callout>}}_1,q_2\right)\cdot ......p\left(q_N|{\mathrm{<figure-callout\; id="1"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">q</figure-callout>}}_1,{\mathrm{<figure-callout\; id="2"\; label="q"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0030.png"\; state="\{\{state\}\}">q</figure-callout>}}_2,...q_{N-1}\right)\\ =p\left(q_1\right){\displaystyle \prod _{n=2}^{N}p\left(q_n|q_{n-1}\right)}\\ ={\mathrm{<figure-callout\; id="1"\; label="\pi "\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">\pi </figure-callout>}}_1{\displaystyle \prod _{n=2}^N{a}_{\left(n-1\right)n}}\end{array}$$

Thus, the probability of detecting a signal object is equal to a sequence of basic signal object prototypes (see also 10 ): $$\begin{array}{l}p\left(G\stackrel{\mathrm{\rho }}{e}|M_j\right)={\displaystyle \sum _{all{Q}_k}p\left(G\stackrel{\mathrm{\rho }}{e}|Q_k,M_j\right)p\left(\stackrel{\mathrm{\rho }}{Q_k}|M_j\right)}\\ ={\displaystyle \sum _{all{Q}_k}\left({\displaystyle \prod _{n=1}^N{b}_n\left(G{e}_n\right)}\right)}\left({\mathrm{<figure-callout\; id="1"\; label="\pi "\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">\pi </figure-callout>}}_1{\displaystyle \prod _{n=2}^N{a}_{\left(n-1\right)n}}\right)\end{array}$$

The most probable signal object model (signal object) for the observed emission Ge is determined by this summation of the individual probabilities over all possible paths Q _k that lead to this observed sequence of signal basic object prototypes Ge. $$\begin{array}{l}p\left(G\stackrel{\mathrm{\rho }}{e}|M_j\right)={\displaystyle \sum _{all{Q}_k}p\left(G\stackrel{\mathrm{\rho }}{e}|Q_k,M_j\right)p\left(\stackrel{\mathrm{\rho }}{Q_k}|M_j\right)}\\ ={\displaystyle \sum _{all{Q}_k}\left({\displaystyle \prod _{n=1}^N{b}_n\left(G{e}_n\right)}\right)}\left({\mathrm{<figure-callout\; id="1"\; label="\pi "\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">\pi </figure-callout>}}_1{\displaystyle \prod _{n=2}^N{a}_{\left(n-1\right)n}}\right)\end{array}$$

The summation over all possible paths Q is not unproblematic due to the possible computational effort. As a rule, it is therefore terminated very early. It is therefore proposed to use only the most probable path Q _k . This is discussed below.

The calculation is done by recursive calculation. The probability a _n (i) of observing the system in state q ⁱ at time n can be calculated as follows: $$\begin{array}{c}{a}_n\left(i\right)=p\left(\mathrm{<figure-callout\; id="1"\; label="G\; e"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">G</figure-callout>}{e}_{}{}_1,\mathrm{<figure-callout\; id="2"\; label="G\; e"\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0030.png"\; state="\{\{state\}\}">G</figure-callout>}{e}_{}{}_2,......G{e}_n;q_n=q^i\right)\equiv p\left(G{e}_i^n,q_n^i\right)\\ a_{n+1}\left(j\right)=\left[{\displaystyle \sum _{i=1}^s{a}_n\left(i\right)\cdot a_{ij}}\right]b_j\left(G{e}_{n+1}\right)\end{array}$$

In this case, summation is carried out over all S possible paths that lead to the state q ⁱ⁺¹

It is assumed that the overall probability of reaching the state q ⁱ _n+1 is dominated by the best path. Then the sum can be simplified with little error. $$a_{n+1}*\left(j\right)=\left[\underset{i}{{\displaystyle \text{Max}}}\left(a_n*\left(i\right)\cdot a_{ij}\right)\right]b_j\left(c_{n+1}\right)$$

Backtracking from the last state now gives the best path.

The probability of this path is a product. Therefore, a logarithmic calculation reduces the problem to a pure summation problem. In this case, the probability of detecting a signal object, which corresponds to the detection of a model M _j , corresponds to the determination of the most probable signal object model for the observed emission X. This is now carried out exclusively via the best possible path Q _best $$p\left(G\stackrel{\mathrm{\rho }}{e}|M_j\right)={\displaystyle \sum _{all{Q}_k}\left({\displaystyle \prod _{n=1}^N{b}_n\left(G{e}_n\right)}\right)\left({\mathrm{<figure-callout\; id="1"\; label="\pi "\; filenames="DE102019009243B3\_0026.png,DE102019009243B3\_0027.png"\; state="\{\{state\}\}">\pi </figure-callout>}}_1{\displaystyle \prod _{n=2}^N{a}_{\left(n-1\right)n}}\right)}$$

This will then close $$\begin{array}{l}p\left(G\stackrel{\mathrm{\rho }}{e}|M_j\right)=p\left(G\stackrel{\mathrm{\rho }}{e}|Q_{best},M_j\right)p\left({\stackrel{\mathrm{\rho }}{Q}}_{best}|M_j\right)\\ =\text{ex}\left(\text{ln}\left({\pi }_1\right)+\text{ln}\left(b_1\left(G{e}_1\right)\right)+{\displaystyle \sum _{n=2}^N\text{ln}\left(b_n\left(G{e}_n\right)\right)+\text{ln}\left(a_{\left(n-1\right)n}\right)}\right)\end{array}$$

It is now of particular importance that the prototype database (115) contains only signal primitive object prototypes.

The detected signal objects are transmitted with the detected parameters instead of the samples. This leads to a compression of the data without leaving the signal character.

IT IS OF PARTICULAR IMPORTANCE THAT NO OBJECT DETECTION TAKES PLACE HERE; THAT ARE AROUND THE VEHICLE. RATHER, STRUCTURES WITHIN THE ULRASONIC ECHO SIGNAL (1) ARE DETECTED AND USED FOR COMPRESSION.

Only this enables the evaluation-free reconstruction of the signal in the control unit after receiving the data.

In contrast to the prior art, it is therefore not the aim of the present proposal to detect and classify objects in the vicinity of the vehicle and thereby bring about data compression, but rather to transmit the ultrasonic echo signal (1) itself with as little loss as possible To compress and transmit the restriction to application-relevant signal form components.

The ultrasonic sensor then transmits the data compressed in this way, preferably only the codes (symbols) of the prototypes recognized in this way, their amplitude and/or time extension and the time of occurrence (time stamp), to the computer system. This minimizes the EMC load caused by the data transmission via the data bus between the ultrasonic sensor and the computer system, and status data from the ultrasonic sensor can be transmitted to the computer system via the data bus between the ultrasonic sensor and the computer system in the time intervals for system error detection, which improves the latency time. When the proposal was being drafted, it was recognized that the transmission of data via the data bus must be prioritized. However, as is known from the prior art, this prioritization does not relate to the prioritization relative to other bus users. Rather, it is the case that the data connection between the sensor and the control computer of the vehicle is usually a point-to-point connection. Here, therefore, the prioritization is to be understood in terms of which datum of the data determined by the sensor system must be transmitted to the control unit first in terms of time. Reports of safety-critical errors in the sensor, in this case the ultrasonic sensor, for example, to the computer system have the highest priority, since they have a high probability of impairing the validity of the measurement data from the ultrasonic sensor. This data is sent from the sensor to the computer system. Requests from the computer system to carry out security-related self-tests have the second highest priority. Such commands are sent from the computer system to the sensor. The third-highest priority is the data from the ultrasonic sensor itself, since the latency must not be increased. All other data has lower priority for transmission over the data bus.

It is particularly advantageous if the method for transmitting sensor data, in particular an ultrasonic sensor, from a sensor to a computer system, in particular in a vehicle, comprising the transmission of an ultrasonic burst with a start (57) and end (56) of the transmission of the Ultrasonic bursts and comprising receiving an ultrasonic signal and forming a received signal for a receiving time (T _E ) at least from the end (56) of the transmission of the ultrasonic burst and comprising transmitting the compressed data via a data bus, in particular a single-wire data bus, to the computer system is designed in such a way that the transmission (54) of the data from the sensor to the computer system begins with a start command (53) from the computer system to the ultrasonic sensor via the data bus and before the end (56) of the transmission of the ultrasonic burst or after a Start command (53) from the computer system to the sensor via the data bus and before the start (57) of the transmission of the ultrasonic burst begins. The transmission (54) then takes place periodically and continuously after the start command (53) up to an end of the data transmission (58). This end of the data transmission (58) is then chronologically after the end of the reception time (T _E ).

A further variant of the proposed method thus provides for the formation of a feature vector signal (stream of feature vectors with n feature vector values and n as the dimension of the feature vector) from the received signal as the first step in data compression. Such a feature vector signal can include multiple analog and digital data signals. It therefore represents a chronological sequence of more or less complex data/signal structures. In the simplest case, it can be understood as a vectorial signal consisting of several partial signals.

For example, it can make sense to form a first and/or higher time derivative of the received signal or the simple or multiple integral of the received signal, which are then partial signals within the feature vector signal.

An envelope signal of the received signal (ultrasonic echo signal) can also be formed, which can then be a partial signal within the feature vector signal (138).

Furthermore, it can be useful to convolve the received signal with the transmitted ultrasound signal and thus form a correlation signal, which can then be a partial signal within the feature vector signal. On the one hand, the signal can be used as an emitted ultrasonic signal, which was used to control the driver for the transmitter, or on the other hand, for example, a signal that was measured at the transmitter and thus better corresponds to the actually radiated sound wave.

Finally, it can be useful to use matched filters to detect the occurrence of predetermined signal objects and to form an optimal filter signal for the respective signal object of some of the predetermined signal objects. A matched filter is a filter that optimizes the signal-to-noise ratio (SNR). The predefined signal objects are to be recognized in the disturbed ultrasound reception signal. The terms correlation filter, signal matched filter (SAF) or just matched filter are also frequently found in the literature. The matched filter is used to optimally determine the presence (detection) of the amplitude and/or position of a known waveform, the predetermined signal object, in the presence of interference (parameter estimation).

This interference can be, for example, signals from other ultrasonic transmitters and/or ground echoes.

The optimal filter output signals are then preferably partial signals within the feature vector signal.

Certain events can be signaled in separate partial signals of the feature vector signal. These events are signal primitives for purposes of this disclosure. Signal basic objects therefore do not include signal shapes, such as square pulses or wavelets or wave trains, but prominent points in the course of the received signal and/or in the course of signals derived from it, such as an envelope signal (ultrasonic echo signal), which, for example, by filtering can be obtained from the received signal.

Another signal, which can be a partial signal of the feature vector signal, can detect, for example, whether the envelope of the received signal, the ultrasonic echo signal, crosses a predetermined third threshold value. It is therefore a signal that signals the presence of a signal basic object within the received signal and thus the feature vector signal.

Another signal, which can be a partial signal of the feature vector signal, can detect, for example, whether the envelope of the received signal, the ultrasonic echo signal, crosses a predetermined fourth threshold value, which can be identical to the third threshold value, in an ascending manner. It is therefore a signal that signals the presence of a signal basic object within the received signal and thus the feature vector signal.

Another signal, which can be a partial signal of the feature vector signal, can detect, for example, whether the envelope of the received signal, the ultrasonic echo signal, crosses a predetermined fifth threshold value, which can be identical to the third or fourth threshold value, in a falling manner . It is therefore a signal that signals the presence of a signal basic object within the received signal and thus the feature vector signal.

Another signal, which can be a partial signal of the feature vector signal, can detect, for example, whether the envelope of the received signal, the ultrasonic echo signal, has a maximum above a sixth threshold value that is identical to the aforementioned third to fifth threshold values can, has. It is therefore a signal that signals the presence of a signal basic object within the received signal and thus the feature vector signal.

Another signal, which can be a partial signal of the feature vector signal, can detect, for example, whether the envelope of the received signal, the ultrasonic echo signal, has a minimum above a seventh threshold value, which is identical to the aforementioned third to sixth threshold values can, has. It is therefore a signal that signals the presence of a signal basic object within the received signal and thus the feature vector signal.

In this case, it is preferably evaluated whether the at least one preceding maximum of the ultrasonic echo signal is at a minimum distance from the minimum in order to avoid detection of noise. Other filters are conceivable at this point. It can also be checked whether the time interval between this minimum and a preceding maximum is greater than a first minimum time interval. The fulfillment of these conditions sets a flag or signal, which itself is preferably a partial signal of the feature vector signal.

It should also be checked in an analogous manner whether the time and amplitude intervals of the other signal objects meet certain plausibility requirements, such as minimum time intervals and/or minimum amplitude intervals. Further analog, binary or digital partial signals can also be derived from these tests, which thus further increase the dimensionality of the feature vector signal.

If necessary, the feature vector signal can also be transformed into a significant feature vector signal, e.g. by a linear mapping or a matrix polynomial of a higher order, in a significance increase stage. In practice, however, it has been shown that this is not yet necessary, at least for today's requirements.

According to the proposed method, signal objects are recognized and classified into recognized signal object classes within the received signal on the basis of the feature vector signal or the significant feature vector signal.

For example, if the amplitude of the output signal of an optimal filter, and thus of a partial signal of the feature vector signal, is above an eighth threshold value that may be specific to an optimal filter, the signal object for which the optimal filter is designed to detect can be considered recognized. In this case, other parameters are preferably also taken into account. If, for example, an ultrasonic burst with increasing frequency was sent during the burst (called chirp-up), an echo is also expected that has this modulation property. If the signal form of the ultrasonic echo signal, for example a triangular signal form of the ultrasonic echo signal, coincides locally in time with an expected signal form, but not the modulation property, then it is not an echo of the transmitter, but a Interfering signal that can come from other ultrasonic transmitters or from overranges. In this respect, the system can then differentiate between intrinsic echoes and extraneous echoes, as a result of which one and the same signal form is assigned to two different signal objects, namely intrinsic echoes and extraneous echoes. The transmission of the intrinsic echoes via the data bus from the sensor to the computer system preferably takes place with priority over the transmission of the external echoes, since the former are generally relevant to safety and the latter are generally not relevant to safety.

Typically, at least one assigned signal object parameter is assigned to each recognized signal object during the detection or determined for this signal object. This is preferably a time stamp that indicates when the object was received. In this case, the time stamp can relate, for example, to the time at which the signal object begins in the received signal or the time it ends or the time position of the time focus of the signal object, etc. Other signal object parameters such as amplitude, aspect ratio, etc. are also conceivable. In one variant of the proposed method, at least one of the assigned signal object parameters is thus transmitted with a symbol for the at least one recognized signal object class. The signal object parameter is preferably a time value as a time stamp and specifies a time position that is suitable for being able to infer the time since the transmission of a previous ultrasonic burst. Preferably, one is determined later from this determined distance of an object as a function of such a determined and transmitted time value.

Finally, the prioritized transmission of the recognized signal object classes follows in the form of associated symbols with time stamps, preferably together with the associated signal object parameters. The transmission can also take place in more complex data structures (records). For example, it is conceivable to first transmit the times of the recognized safety-relevant signal objects (e.g. identified obstacles) and then the recognized signal object classes of the safety-relevant signal objects. This further reduces the latency.

At least in one variant, the proposed method includes determining a chirp value as an assigned signal object parameter, which indicates whether the detected signal object is an echo of an ultrasonic transmission burst with chirp up or a chirp down or a no- chirp properties. Chirp-up means that the frequency within the received signal object increases in the received signal. Chirp down means that the frequency within the received signal object falls in the received signal. No-chirp means that the frequency within the received signal object remains essentially the same in the received signal. At this point I would like to refer to the as yet unpublished German patent applications DE 10 2017 100 837 .3 and DE 10 2017 100 835.7 pointed out, which are an integral part of this disclosure.

In a variant of the method, a confidence signal is thus also generated by forming the correlation, e.g. by forming a time-continuous or time-discrete correlation integral, between the received signal or instead of the received signal with a signal derived from the received signal on the one hand and a reference signal, for example the ultrasonic transmission signal or a other expected wavelet, formed on the other hand. The confidence signal is then typically a partial signal of the feature vector signal, that is to say a component of the feature vector which consists of a sequence of vector sampling values (feature vector values).

In a variant of the method, a phase signal is also formed on this basis, which indicates the phase shift, for example of the received signal or a signal formed from it (e.g. the confidence signal) compared to a reference signal, for example the ultrasonic transmission signal and/or another reference signal. The phase signal is then typically also a partial signal of the feature vector signal, that is to say a component of the feature vector, which consists of a sequence of vector sampling values.

Similarly, in a further variant of the proposed method, a phase confidence signal can be formed by forming the correlation between the phase signal or a signal derived therefrom and a reference signal and used as a partial signal of the feature vector signal. The phase confidence signal is then typically also a partial signal of the feature vector signal, that is to say a component of the feature vector, which consists of a sequence of vector samples.

When evaluating the feature vector signal, it then makes sense to compare the phase confidence signal with one or more threshold values for generating a discretized phase confidence signal, which itself becomes a partial signal of the feature vector signal can.

In one variant of the proposed method, the feature vector signal and/or the significant feature vector signal can be evaluated in such a way that one or more distance values are formed between the feature vector signal and one or more signal object prototype values for recognizable signal object classes . Such a distance value can be boolean, binary, discrete, digital or analog. All distance values are preferably combined with one another in a non-linear function. In the case of an expected chirp-up echo in the form of a triangle, a received chirp-down echo in the form of a triangle can be discarded. This discarding is a non-linear process within the meaning of this disclosure.

Conversely, the triangle in the received signal can be different. First of all, this affects the amplitude of the triangle in the received signal. If the amplitude in the received signal is sufficient, the optimum filter assigned to this triangular signal, for example, supplies a signal above a predetermined ninth threshold value. In that case, for example, a recognized signal object can be assigned to this signal object class (triangular signal) at this point in time when it was exceeded. In this case, the distance value between the feature vector signal and the prototype (here the ninth threshold value) one or more predetermined, binary, digital or analog distance values (here 0=crossing).

In a further variant of the method, at least one signal object class is wavelets, which are estimated by estimation devices (e.g. optimal filters) and/or estimation methods (e.g. estimation programs running in a digital signal processor) and are thus detected. The term wavelet designates functions that can be used as a basis for a continuous or a discrete wavelet transformation. The word "wavelet" is a neologism of the French "ondelette" meaning "small wave" and translated into English partly literally ("onde"→"wave") and partly phonetically ("-lette"→"-let") became. The expression "wavelet" was coined in the 1980s in geophysics (Jean Morlet, Alex Grossmann) for functions that generalize the short-time Fourier transform, but since the late 1980s it has been used exclusively in its current meaning. In the 1990s there was a veritable wavelet boom, triggered by the discovery of compact, continuous (up to any order of differentiability) and orthogonal wavelets by Ingrid Daubechies (1988) and the development of the fast wavelet transform (FWT) algorithm using MultiResolution Analysis (MRA) by Stephane Mallat and Yves Meyer (1989).

In contrast to the sine and cosine functions of the Fourier transform, the most commonly used wavelets are not only local in the frequency spectrum, but also in the time domain. “Locality” is to be understood in the sense of small scatter. The probability density is the normalized square of the absolute value of the function under consideration or of its Fourier transform. The product of both variances is always greater than a constant, analogous to Heisenberg's uncertainty principle. From this limitation, the Paley-Wiener theory (Raymond Paley, Norbert Wiener), a forerunner of the discrete wavelet transformation, and the Calderön-Zygmund theory (Alberto Calderön, Antoni Zygmund), which transformation corresponds.

In technical terms, the integral of a wavelet function is always 0, which is why the wavelet function usually takes the form of outwardly tapering (decreasing) waves (i.e. “little waves” = ondelettes = wavelets). However, within the meaning of this disclosure, wavelets that have an integral other than 0 should also be permissible. The rectangular and triangular wavelets described below should be mentioned here as an example. This further interpretation of the term "wavelet" is common in the American-speaking world and is therefore well known. This further interpretation should also apply here.

Important examples of wavelets with a 0-integral are the Haar wavelet (Alfred Haar 1909), the Daubechies wavelets named after Ingrid Daubechies (around 1990), the Coiflet wavelets also constructed by her and the more theoretically significant Meyer wavelet (Yves Meyer, around 1988).

There are wavelets for spaces of any dimension, usually a tensor product of a one-dimensional wavelet basis is used. Due to the fractal nature of the two-scale equation in MRA, most wavelets have complicated shapes, most are not closed-form. This is of particular importance because the feature vector signal mentioned above is multidimensional and therefore allows the use of multidimensional wavelets for signal object detection.

A special variant of the proposed method is therefore the use of multidimensional wavelets with more than two dimensions for signal object detection. In particular, the use of appropriate optimal filters for detecting such wavelets with more than two dimensions is proposed in order to optionally supplement the feature vector signal with further partial signals suitable for detection.

A particularly suitable wavelet is, for example, a triangular wavelet. This is characterized by a start time of the triangular wavelet, a temporally essentially linear increase in the wavelet amplitude following the start time of the triangular wavelet up to a maximum of the amplitude of the triangular wavelet and a maximum of the triangular wavelet and a chronologically subsequent, chronologically essentially linear drop in the wavelet amplitude up to one end of the triangular wavelet.

Another particularly suitable wavelet is a rectangular wavelet, which also includes trapezoidal wavelets within the meaning of this disclosure. A rectangular wavelet is characterized by a starting time of the rectangular wavelet, which is an increase in the wavelet amplitude of the rectangular wavelet with a first temporal steepness of the rectangular wavelet up to a first plateau time of the rectangular wave lets follow. The first plateau time of the rectangular wavelet is followed by a persistence of the wavelet amplitude with a second steepness of the wavelet amplitude up to a second plateau time of the rectangular wavelet. The second plateau point in time of the rectangular wavelet is followed by a fall with a third steepness over time up to the end of the rectangular wavelet. The amount of the second slope over time is less than 10% of the amount of the first slope over time and less than 10% of the amount of the third slope over time.

Instead of the wavelets described above, it is also possible to use other two-dimensional wavelets, such as a sine half-wave wavelet, which also has an integral not equal to 0.

It is proposed that when wavelets are used, the time shift of the relevant wavelet of the recognized signal object is used as a signal object parameter. For example, this shift can be determined by correlation. It is further proposed that when wavelets are used, the point in time when the level of the output of an optimal filter suitable for detecting the relevant wavelet exceeds a predefined tenth threshold value for this signal object or this wavelet is preferably used. The ultrasonic echo signal (the envelope curve) of the received signal and/or a phase signal and/or a confidence signal etc. are preferably evaluated.

Another possible signal object parameter that can be determined is a temporal compression or expansion of the relevant wavelet of the detected signal object. Likewise, an amplitude of the wavelet of the detected signal object can be determined.

When developing the proposal for the method disclosed here, it was recognized that it is advantageous to first transmit the data of the detected signal objects of the very quickly arriving echoes from the sensor to the computer system and only then the subsequent data of the signal objects detected later. Preferably, at least the recognized signal object class and a time stamp are always transmitted, which should preferably indicate when the signal object arrived at the sensor again. As part of the recognition process, scores can be assigned to the various signal objects that come into question for a section of the received signal, which scores indicate which probability is assigned to the presence of this signal object according to the estimation algorithm used. Such a score is binary in the simplest case. However, it is preferably a complex, real or integer number. It can be the determined distance, for example. If several signal objects have a high score value, it makes sense in some cases to also transfer the data of recognized signal objects with lower score values. In order to enable the computer system to handle it correctly, not only the date (symbol) of the detected signal object and the time stamp for the respective signal object should be transmitted in this case, but also the determined score value. Instead of only transmitting the date (symbol) of the recognized signal object and the time stamp for the signal object corresponding to this symbol, the date (symbol) of the signal object with the second smallest distance and its time stamp for the signal object corresponding to this second most likely symbol can also be transmitted. In this case, a list of hypotheses consisting of two recognized signal objects and their temporal positions as well as additionally assigned score values is thus transmitted to the computer system. Of course, a hypothesis list consisting of more than two symbols for more than two recognized signal objects and their temporal positions as well as additionally assigned score values is also transmitted to the computer system.

The data of the recognized signal object class and the assigned data such as time stamps and score values of the respective recognized signal object classes, ie the assigned signal object parameters, are preferably transmitted according to the FIFO principle. This ensures that the data of the reflections of the closest objects are always transmitted first, so that the safety-critical case of the vehicle colliding with an obstacle is prioritized according to probability.

In addition to the transmission of measurement data, error states of the sensor can also be transmitted. This can also happen during a receive time (T _E ) when the sensor determines through self-testing devices that there is a defect and the previously transmitted data could potentially be erroneous. This ensures that the computer system can learn about a change in the evaluation of the measurement data at the earliest possible point in time and can discard it or treat it differently. This is of particular importance for emergency braking systems, since emergency braking is a safe is a safety-critical intervention that may only be initiated if the underlying data has a corresponding trust value. In contrast, the transmission of the measurement data, ie, for example, the date of the recognized signal object class and/or the transmission of the one assigned signal object parameter, is therefore postponed and therefore given a lower priority. It is of course conceivable that the transmission would be aborted if an error occurred in the sensor. In some cases, however, it can happen that an error appears possible but is not definitely present. In this respect, the continuation of a transfer may be indicated in such cases. The transmission of safety-critical errors of the sensor is therefore preferably carried out with a higher priority.

In addition to the wavelets already described with an integration value of 0 and the signal sections additionally referred to here as wavelets with an integration value other than 0, certain points in time in the course of the received signal can also be regarded as a basic signal object within the meaning of this disclosure, which can be used for data compression and instead of samples of the received signal can be transmitted. In the following, we refer to this subset of the set of possible signal basic objects as signal instants. The signal times are therefore a special form of the signal basic objects in the sense of this disclosure.

A first possible signal point in time and thus a signal base object is when the amplitude of the ultrasonic echo signal (1) crosses the amplitude of an eleventh threshold value signal (SW) in an increasing direction.

A second possible signal point in time and thus a basic signal object is when the amplitude of the ultrasonic echo signal (1) crosses the amplitude of a twelfth threshold value signal (SW) in a descending direction.

A third possible signal point in time and thus a signal base object is a maximum of the amplitude of the ultrasonic echo signal (1) above the amplitude of a thirteenth threshold value signal (SW).

A fourth possible signal point in time and thus a signal base object is a minimum of the amplitude of the ultrasonic echo signal (1) above the amplitude of a fourteenth threshold value signal (SW).

It may be useful to use signal-time-type-specific threshold value signals (SW) for these four exemplary types of signal times and other types of signal times.

1. 1. das Auftreten eines ersten möglichen Signalzeitpunkts mit einem Kreuzen der Amplitude des Ultraschall-Echo-Signals (1) mit der Amplitude eines Schwellwertsignals (SW) in aufsteigender Richtung und daran zeitlich anschließend
2. 2. das Auftreten eines dritten möglichen Signalzeitpunkts mit einem Maximum der Amplitude des Ultraschall-Echo-Signals (1) oberhalb der Amplitude eines Schwellwertsignals (SW) und daran zeitlich anschließend
3. 3. das Auftreten eines zweiten möglichen Signalzeitpunkts mit einem Kreuzen der Amplitude des Ultraschall-Echo-Signals (1) mit der Amplitude eines Schwellwertsignals (SW) in absteigender Richtung

in zeitlicher Korrelation zum Überschreiten des besagten Mindestpegels am Ausgang des besagten Optimal-Filters erwartet werden. Das beispielhafte Signalobjekt eines Dreiecks-Wavelets besteht in diesem Beispiel also in der vordefinierten Abfolge dreier Signalgrundobjekte, die erkannt, durch ein Symbol ersetzt wird und als dieses Symbol bevorzugt zusammen mit seinem Auftretenszeitpunkt, dem Zeitstempel, übertragen wird. Dieses Überschreiten des besagten Mindestpegels am Ausgang des besagten Optimal-Filters ist übrigens ein weiteres Beispiel für einen fünften möglichen Signalzeitpunkt und damit ein weiteres mögliches Signalgrundobjekt.The chronological sequence of signal basic objects is typically not arbitrary. This is used in this disclosure, since it is preferably not the basic signal objects, which are of a simpler nature, that are to be transmitted, but rather recognized patterns of sequences of these basic signal objects, which then represent the actual signal objects. If, for example, a triangular wavelet of sufficient amplitude is expected in the ultrasonic echo signal (1), then in addition to a corresponding minimum level at the output of an optimum filter suitable for detecting such a triangular wavelet

1. 1. the occurrence of a first possible signal point in time with a crossing of the amplitude of the ultrasonic echo signal (1) with the amplitude of a threshold value signal (SW) in an increasing direction and subsequent thereto in time
2. 2. the occurrence of a third possible signal time point with a maximum of the amplitude of the ultrasonic echo signal (1) above the amplitude of a threshold value signal (SW) and subsequent thereto in time
3. 3. the occurrence of a second possible signal time with a crossing of the amplitude of the ultrasonic echo signal (1) with the amplitude of a threshold value signal (SW) in a descending direction

are expected in temporal correlation to exceeding said minimum level at the output of said optimal filter. In this example, the exemplary signal object of a triangular wavelet consists of the predefined sequence of three basic signal objects, which are recognized, replaced by a symbol and transmitted as this symbol, preferably together with its time of occurrence, the time stamp. Incidentally, this exceeding of the said minimum level at the output of the said optimal filter is a further example of a fifth possible signal point in time and thus a further possible signal basic object.

The thus resulting grouping and chronological sequence of recognized signal basic objects can itself, for example by a Viterbi decoder, as a predefined, expected grouping or chronological sequence of signal basic objects can be recognized and can thus represent a signal basic object again. A sixth possible signal point in time and thus a basic signal object is the occurrence of such a predefined grouping and/or chronological sequence of other basic signal objects.

If such a grouping of basic signal objects or a chronological sequence of such signal object classes is recognized in the form of a signal object, the symbol of this recognized summary signal object class and at least one assigned signal object parameter are preferably transmitted instead of the transmission of the individual basic signal objects, since this saves considerable data bus capacity. However, there may be cases where both are transmitted. In this case, the data (symbol) of the signal object class of a signal object is transmitted, which is a predefined time sequence and/or grouping of other signal basic objects. In order to achieve compression, it is advantageous if at least one signal object class (symbol) of at least one of these other signal basic objects is not transmitted.

A chronological grouping of basic signal objects is present in particular when the chronological distance between these basic signal objects does not exceed a predefined distance. In the example mentioned above, the propagation time of the signal in the matched filter should be considered. Typically, the matched filter should be slower than the comparators. The change in the output signal of the optimum filter should therefore be in a fixed temporal relationship to the occurrence of the relevant signal times.

A method is therefore proposed here for the transmission of sensor data, in particular an ultrasonic sensor, from a sensor to a computer system, in particular in a vehicle, which starts with the transmission of an ultrasonic burst and the reception of an ultrasonic signal and the formation of a time-discrete received signal consisting of a sequence of samples begins. A date (time stamp) is assigned to each sampling value. At least two parameter signals are then determined, each relating to the presence of a basic signal object associated with the respective parameter signal, using at least one suitable filter (e.g. an optimal filter) from the sequence of sampled values of the received signal. The resulting parameter signals (feature vector signals) are also in the form of a time-discrete sequence of respective parameter signal values (feature vector values), which are each correlated with a date (time stamp). Thus, each parameter signal value (feature vector value) is preferably assigned exactly one chronological data item (time stamp). These parameter signals together are referred to below as the feature vector signal. The feature vector signal is thus in the form of a time-discrete sequence of feature vector signal values each having n parameter signal values, which consist of the parameter signal values and further parameter signal values each having the same date (time stamp). Here n is the dimensionality of the individual feature vector signal values, which are preferably the same from one feature vector value to the next feature vector value. This respective time data (time stamp) is assigned to each feature vector signal value formed in this way. This is followed by the evaluation of the time profile of the feature vector signal in the resulting n-dimensional phase space and the conclusion that a signal object has been recognized, with the determination of an evaluation value (e.g. the distance). As explained above, a signal object consists of a chronological sequence of basic signal objects. A predefined symbol is typically assigned to the signal object. Figuratively speaking, it is checked here whether the point to which the n-dimensional feature vector signal points in the n-dimensional phase space, on its way through the n-dimensional phase space in a predetermined time sequence, is at predetermined points in this n-dimensional phase space closer than a predetermined maximum distance approaches. The feature vector signal thus has a course over time. An evaluation value (e.g. a distance) is then calculated, which can, for example, reflect the probability of the existence of a specific sequence. This evaluation value, which is again assigned a date (time stamp), is then compared with a threshold value vector, forming a Boolean result that can have a first and a second value. If this Boolean result has the first value for this chronological data (time stamp), the symbol of the signal object and the chronological data (time stamp) assigned to this symbol are transmitted from the sensor to the computer system. If necessary, further parameters can be transmitted depending on the detected signal object.

The reconstruction of a reconstructed ultrasonic echo signal model (610) from the detected signal objects (122) is particularly advantageous. This ultrasonic echo signal model (610) is generated from the summing, linearly superimposed signal curve models of the individual recognized signal objects. It is then subtracted from the ultrasonic echo signal (1). A residual signal (660) is thereby formed. As a result, signal objects similar to the selected signal object recognized with a higher probability are better suppressed. The weaker signal objects appear in the residual signal (660) stand out better and can be recognized better. (See also 18 ). A method according to the invention therefore preferably also provides for a subtraction of the ultrasonic echo signal model (610) reconstructed from the detected signal objects from the ultrasonic echo signal (1) to form a residual signal (660). The residual signal (660) formed in this way is then used again to form the feature vector signal (138) and the next signal object is recognized with the next higher probability. Since the signal object detected first was removed from the input signal according to its weighting, it can no longer influence this detection. Thus, this form of recognition delivers a better result.

However, this detection method is usually slower. Therefore, it makes sense to carry out a first direct object recognition without subtraction while the measurement is still running and then, after recording all sampled values of an ultrasonic echo response of the surroundings of the vehicle, after the emission of an ultrasonic pulse or an ultrasonic burst, a repeated pattern recognition with subtraction of the ultrasonic echo signal model (610), which takes longer but is more precise.

This reducing classification of the ultrasonic echo signal (1) into signal objects using the ultrasonic echo signal model (610) is preferably ended when the absolute values of the sampled values of the residual signal (660) are below the absolute values of a predetermined threshold value curve.

In a particularly preferred manner, the data is transmitted in the vehicle via a serial, bidirectional single-wire data bus. In this case, the electrical return line is preferably ensured by the body of the vehicle. The sensor data are preferably sent to the computer system in a current-modulated manner. The data for controlling the sensor are preferably sent to the sensor in voltage-modulated form by the computer system. According to the invention, it was recognized that the use of a PSI5 data bus and/or a DSI3 data bus is particularly suitable for data transmission. Furthermore, it was recognized that it is particularly advantageous to transmit the data to the computer system at a transmission rate of> 200 kbit/s and from the computer system to the at least one sensor at a transmission rate> 10 kbit/s, preferably 20 kbit/s . Furthermore, it was recognized that the transmission of data from the sensor to the computer system should be modulated with a transmission current on the data bus, the current strength of which should be less than 50 mA, preferably less than 5 mA, preferably less than 2.5 mA. These buses must be adjusted accordingly for these operating values. However, the basic principle remains the same.

• • Optimalfilter,
• • Komparatoren,
• • Schwellwertsignalerzeugungsvorrichtungen zur Erzeugung eines oder mehrerer Schwellwertsignale (SW),
• • Differenzierer zur Bildung von Ableitungen,
• • Integrierer zur Bildung integrierter Signale,
• • sonstige Filter,
• • Hüllkurvenformer zur Erzeugung eines Hüllkurvensignals (Ultraschall-Echo-Signal) aus dem Empfangssignal,
• • Korrelationsfilter zum Vergleich des Empfangssignals oder von daraus abgeleiteten Signalen mit Referenzsignalen.

A computer system with a data interface to said data bus, preferably said single-wire data bus, is required to carry out the method described above, which supports the decompression of the data compressed in this way. As a rule, however, the computer system will not carry out a complete decompression but, for example, will only evaluate the time stamp and the recognized signal object type. The sensor, which is required to carry out one of the methods described above, has at least one transmitter and at least one receiver for generating a received signal, which can also be combined as one or more transducers. Furthermore, it has at least devices for processing and compressing the received signal and a data interface for transmitting the data via the data bus, preferably the said single-wire data bus, to the computer system. For compression, the compression device preferably has at least one of the following sub-devices:

• • optimal filter,
• • comparators,
• • Threshold signal generating devices for generating one or more threshold signals (SW),
• • Differentiator for the formation of derivations,
• • integrator for forming integrated signals,
• • other filters,
• • Envelope shaper for generating an envelope signal (ultrasonic echo signal) from the received signal,
• • Correlation filter for comparing the received signal or signals derived from it with reference signals.

• Es beginnt mit dem Aussenden eines Ultraschall-Bursts. Es folgt das Empfangen eines Ultraschallsignals, also typischerweise eine Reflektion und das Bilden eines zeitdiskreten Empfangssignals bestehend aus einer zeitlichen Folge von Abtastwerten. Dabei ist jedem Abtastwert ein zeitliches Datum (Zeitstemple) zugeordnet. Dieses gibt den Zeitpunkt der Abtastung typischerweise wieder. Auf Basis dieses Datenstroms erfolgt die Bestimmung eines ersten Parametersignals einer ersten Eigenschaft mit Hilfe eines ersten Filters aus der Folge von Abtastwerten des Empfangssignals. Bevorzugt wird dabei das Parametersignal wieder als zeitdiskrete Folge von Parametersignalwerten ausgebildet. Jedem Parametersignalwert wird wieder genau ein zeitliches Datum (Zeitstempel) zugeordnet. Bevorzugt entspricht dieses Datum dem jüngsten zeitlichen Datum eines Abtastwertes, der zur Bildung dieses jeweiligen Parametersignalwertes benutzt wurde. Zeitlich parallel dazu erfolgt bevorzugt die Bestimmung mindestens eines weiteren Parametersignals einer diesem weiteren Parametersignal zugeordneten Eigenschaft mit Hilfe eines diesem weiteren Parametersignal zugeordneten weiteren Filters aus der Folge von Abtastwerten des Empfangssignals, wobei die weiteren Parametersignale jeweils wieder als zeitdiskrete Folgen von weiteren Parametersignalwerten ausgebildet sind. Auch hier wird jedem weiteren Parametersignalwert jeweils das gleiche zeitliche Datum (Zeitstempel) wie dem entsprechenden Parametersignalwert zugeordnet.

In a particularly simple form, the proposed method for transmitting sensor data, in particular an ultrasonic sensor, from a sensor to a computer system, in particular in a vehicle, is carried out as follows:

• It begins by emitting an ultrasonic burst. An ultrasonic signal is then received, ie typically a reflection and the formation of a time-discrete received signal consisting of a temporal sequence of sampled values. In this case, each sampling value is assigned a date (time stamp). This typically reflects the time of sampling. On the basis of this data stream, a first parameter signal of a first property is determined with the aid of a first filter from the sequence of samples of the received signal. In this case, the parameter signal is preferably in the form of a time-discrete sequence of parameter signal values. Exactly one date (time stamp) is assigned to each parameter signal value. This date preferably corresponds to the most recent chronological date of a sample value that was used to form this respective parameter signal value. Simultaneously in time, at least one further parameter signal of a property assigned to this further parameter signal is preferably determined with the aid of a further filter assigned to this further parameter signal from the sequence of sampled values of the received signal, with the further parameter signals each being in the form of time-discrete sequences of further parameter signal values. Here, too, each additional parameter signal value is assigned the same time date (time stamp) as the corresponding parameter signal value.

The first parameter signal and the further parameter signals are collectively referred to below as a feature vector signal. This feature vector signal (or also parameter vector signal) thus represents a time-discrete sequence of feature vector signal values, which consist of the parameter signal values and further parameter signal values each with the same chronological date (time stamp). Thus, each feature vector signal value (=parameter signal value) formed in this way can be assigned this respective chronological data (time stamp).

The feature vector signal values of a date (time stamp) are then preferably compared quasi-continuously with a threshold vector, which is preferably a prototype vector, with formation of a Boolean result that can have a first and a second value. For example, it is conceivable compare the magnitude of the current feature vector signal value, representing, for example, a first component of a feature vector signal value, with a fifteenth threshold value, representing a first component of the threshold vector, and set the Boolean result to a first value if the magnitude of the feature vector signal value is less than this fifteenth threshold, and to a second value if it is not. If the Boolean result has a first value, then it is also conceivable to compare the absolute value of the additional feature vector signal value, which represents another component of this feature vector signal, for example, with an additional threshold value, which represents an additional component of the threshold value vector , to compare and leave the boolean result at the first value if the magnitude of the further feature vector signal value is less than this further threshold and to set the boolean result to the second value if this is not the case. In this way, all further feature vector signal values can be checked. Of course, other classifiers are also conceivable. A comparison with several different threshold value vectors is also possible. These threshold value vectors therefore represent the prototypes of predetermined signal forms. They come from the said library. A symbol is again preferably assigned to each threshold value vector.

In this case, the last step is the transmission of the symbol and possibly also the feature vector signal values and the time date (time stamp) assigned to this symbol or feature vector signal value from the sensor to the computer system if the Boolean result has the first value for this chronological date (time stamp).

This means that all other data is no longer transmitted. Furthermore, the multi-dimensional evaluation avoids interference.

On this basis, a sensor system is therefore proposed, with at least one computer system that is able to carry out one of the methods presented above and with at least two sensors that are also able to carry out one of the methods presented above, so that these at least two sensors can communicate with the computer system by signal object detection and are also able to transmit extraneous echoes in a compact form and to make this information available to the computer system as well. Accordingly, the sensor system is typically provided for data transmission between the at least two sensors and the computer system runs or can run in accordance with the procedures previously described. Within the at least two sensors of the sensor system, one ultrasound reception signal, ie at least two ultrasound reception signals, is typically compressed using one of the previously proposed methods and transmitted to the computer system. In this case, the at least two received ultrasound signals are reconstructed into reconstructed received ultrasound signals within the computer system. The computer system then carries out object recognition of objects in the vicinity of the sensors with the aid of reconstructed ultrasound reception signals. In contrast to the prior art, the sensors do not carry out this object recognition.

The computer system preferably additionally carries out an object recognition of objects in the vicinity of the sensors with the aid of the reconstructed ultrasound reception signals and additional signals from other sensors, in particular the signals from radar sensors.

As a final step, the computer system preferably creates an environment map for the sensors or a device, of which the sensors are a part, based on the detected objects.

The proposed ultrasonic sensor system, as exemplified in the 7 and 12 is shown, thus preferably comprises an ultrasonic transducer (100), a feature extractor (111) and with an estimator (150, 151) or classifier and a physical interface (101). The ultrasonic transducer (100) is preferably provided and/or set up to receive an acoustic ultrasonic wave signal and to form an ultrasonic transducer signal (102) as a function thereof. The feature vector extractor (111) is provided and/or set up to form a feature vector signal (138) from the ultrasonic transducer signal (102). The ultrasonic sensor system is preferably set up and provided to use the estimator (151, 150) to recognize signal objects in the ultrasonic echo signal (1) and to classify them into signal object classes, it also being possible for a signal object class to include just one signal object. Each signal object (122) recognized and classified in this way is preferably assigned at least one assigned signal object parameter and a symbol corresponding to the signal object class assigned to this signal object, or at least one assigned signal object parameter and a symbol are determined for this signal object for each signal object (122) thus recognized and classified. At least the symbol of a recognized signal object class and at least one associated signal object parameter of this recognized signal object class are transmitted to a higher-level computer system via a data bus.

The estimator (150) preferably has a distance determiner (112) and a prototype database (115). Likewise, the estimator (150) preferably has a Viterbi estimator and a signal object database. Also, the estimator (151) can use a neural network model.

• • Warten auf ein Ultraschallempfangssignal, das vom Grundrauschen verschieden ist,
• • Wechsel in einen Zustand „kein Signalobjektgrundprototyp“ falls das Ultraschallempfangssignal von Grundrauschen verschieden ist, und Durchführung eines Verfahrens zum Erkennen und zum Klassifizieren von Signalgrundprototypen;
• • Wechsel in eine Folge von Zuständen, die den Signalgrundprototypen einer vorgegebenen Folge von Signalgrundprototypen zugeordnet sind, wenn der erste Signalgrundprototyp dieser Folge erkannt wird;
• • Folgen der Folge der Signalgrundprototypen bis das Ende der Folge von Signalgrundprototypen erreicht ist;
• • Schließen auf das Vorliegen eines dieser Folge von Signalgrundprototypen und auf ein dieser Folge zugeordnetes Signalobjekt, wenn das Ende dieser Folge von Signalgrundprototypen erreicht ist und Signalisierung dieses Signalobjekts;
• • Abbruch (nicht in 11 eingezeichnet) dieser Folge bei Zeitüberschreitung und/oder bei einfache oder mehrfacher Detektion eines Signalgrundprototypen an einer Position dieser Folge, an der dieser Signalgrundprototyp nicht erwartet wird oder an der in der folgenden Position dieser Signalgrundprototyp nicht erwartet wird.
• • Rücksprung in den Zustand „kein Signalobjektgrundprototyp“.

A proposed method for operating an ultrasonic sensor therefore includes the steps accordingly 11 :

• • Waiting for an ultrasonic received signal that is different from the noise floor,
• • Changing to a state “no signal object basic prototype” if the received ultrasonic signal differs from background noise, and carrying out a method for recognizing and classifying basic signal prototypes;
• • changing to a sequence of states associated with the basic signal prototypes of a given sequence of basic signal prototypes when the first basic signal prototype of this sequence is recognized;
• • following the sequence of signal base prototypes until the end of the sequence of signal base prototypes is reached;
• • Inferring the presence of one of this sequence of basic signal prototypes and a signal object associated with this sequence when the end of this sequence of basic signal prototypes has been reached and signaling of this signal object;
• • Abort (not in 11 shown) of this sequence if the time is exceeded and/or if a basic signal prototype is detected once or multiple times at a position in this sequence where this basic signal prototype is not expected or at which this basic signal prototype is not expected in the following position.
• • Return to the status "no signal object basic prototype".

The compression method described above corresponds to an associated decompression method, which is preferably used for decompressing compressed in this signal-object-oriented manner and transmitted ultrasound reception data is used in the control computer after receipt by the sensor via said data interface. After receiving new data to be decompressed in the control computer coming from the sensor, an ultrasonic echo signal model is provided that initially has no signal ( 15a) . This ultrasonic echo signal model is now slowly filled in for each signal object by successive addition of the respective prototypical, parameterized signal curve of the signal objects (160) contained in the ultrasonic reception data, the ultrasonic echo signal model (610) and the measured one Signal curve approximated. Preferably, only the signal curves of signal objects are added that meet the specified conditions, such as a specified chirp direction ( 13b) . Of course, summing up without a selection condition is also possible. ( 15 and 16 ) From the summed up signal curves of the signal curves of the prototypical signal objects recognized by the sensor, the reconstructed ultrasonic received signal is preferably formed on the basis of the ultrasonic echo signal model in the control computer of the sensor, which in the form of the formation of sampled values of a reconstructed envelope curve of the received signal (ultrasound -echo signal) can happen. This reconstructed ultrasonic reception signal (reconstructed ultrasonic echo signal) can then be used much better in the control computer for more complicated processes such as sensor fusion than the supposed object data that are transmitted by the sensor to the control unit.

advantage of the invention

Such a compressed transmission of data via the data bus between the sensor and the computer system reduces the data bus load and thus the criticality with regard to EMC requirements and also creates free data bus capacities during the reception time for the transmission of control commands from the computer system to the sensor and for the transmission of status information and other data from the sensor to the computer system. The proposed prioritization ensures that safety-relevant data is transmitted first, so there are no unnecessary dead times for the sensor.

character list

• 1 shows the basic process of signal compression and transmission (description see above);
• 2 shows in more detail the basic process of signal compression and transmission (description see above);
• 3 a shows a conventional ultrasonic echo signal (1) and its conventional evaluation.
• 3 b shows a conventional ultrasonic echo signal (1) and its evaluation, with the amplitude also being transmitted.
• 3c shows an ultrasonic echo signal with the chirp direction included.
• 3d shows detected signal objects (triangle signals) in the signal of 1c discarding unrecognized signal components.
• 4e shows the unclaimed conventional transmission
• 4f shows the unclaimed transmission of analyzed data after complete reception of the ultrasonic echo
• 4g shows the claimed transmission of compressed data, in this example symbols for signal basic objects largely without compression according to the prior art.
• 5h shows the claimed transmission of compressed data, symbols for signal basic objects being compressed into symbols for signal objects in this example.
• 6 shows the claimed transmission of compressed data, symbols for signal basic objects being compressed into symbols for signal objects in this example. Not only the envelope signal (ultrasonic echo signal) but also the confidence signal is evaluated.
• 7 shows an exemplary device in the form of an ultrasonic sensor with detection of signal objects, the data interface, the data bus and the control unit are not shown for the sake of simplicity.
• 8th serves to explain the selection of the signal basic object prototypes of the prototype database (115) by the distance finder (112). 9 serves to explain the HMM method used by the Viterbi estimator (113) to identify the signal object as the most probable sequence of signal basic object prototypes based on a sequence of recognized signal basic object prototypes (121) as a recognized signal object (122).
• 10 shows an exemplary state sequence in the Viterbi estimator (113) for the detection of a single signal object.
• 11 shows an exemplary, preferred state sequence in the Viterbi estimator (113) for the continuous recognition of one of signal objects, as is typically necessary for the recognition of autonomous driving tasks.
• 12 Figure 1 shows an alternative embodiment with an estimator (151) executing a neural network model.
• 13 shows the decompression of the transmitted envelope of the ultrasonic signal (ultrasonic echo signal) restricted to the chirp-down signal components;
• 14 shows the decompression of the transmitted envelope of the ultrasonic signal (ultrasonic echo signal) limited to the chirp-up signal component;
• 15 and 16 show the decompression of the transmitted envelope of the ultrasonic signal (ultrasonic echo signal) with chirp-up signal components and chirp-down signal components;
• 17 shows the original signal (a), the reconstructed signal (b) and their superimposition (c).
• 18 shows an improved device for improved compression.

Description of the other figures

The 1 and 2 have already been described above.

Figure 3a

3a shows the time course of a conventional ultrasonic echo signal (1) and its conventional evaluation, not claimed, in freely chosen units. Starting with the transmission of the transmission burst (SB), a threshold value signal (SW) is carried along. Whenever the envelope signal (ultrasonic echo signal (1)) of the ultrasonic received signal exceeds the threshold value signal (SW), the output (2) is set to logic 1. It is a temporal analog interface with a digital output level. The further evaluation is then taken over in the control unit of the sensor. Signaling errors or controlling the sensor is not possible via this analog interface, which corresponds to the prior art.

Figure 3b

3b shows the time course of a conventional ultrasonic echo signal (1) and its conventional evaluation, not claimed, in freely chosen units. Starting with the transmission of the transmission burst (SB), a threshold value signal (SW) is carried along. However, whenever the envelope signal (ultrasonic echo signal (1)) of the ultrasonic received signal exceeds the threshold signal (SW), the output (2) is now set to a level corresponding to the amplitude of the detected reflection. It is a temporal analog interface with an analog output level. The further evaluation is then taken over in the control unit of the sensor. Signaling errors or controlling the sensor is not possible via this analog interface, which corresponds to the prior art.

Figure 3c

shows the ultrasonic echo signal for explanation, with the chirp direction (e.g. A=chirp up; B=chirp down) being marked.

Figure 3d

In 3d the principle of symbolic signal transmission is explained. Instead of the signal off 3c only two types of triangle objects are transferred here as an example. Specifically, these are a first th triangle object (A) for the chirp up case and a second triangle object (B) for the chirp down case. At the same time, the point in time and the amplitude of the triangular object are transmitted. If the signal is now reconstructed on the basis of this data, a corresponding signal is obtained 3d . The signal components that did not correspond to the triangular signals were removed from this signal. This means that unrecognized signal components are discarded and data is massively compressed.

Figure 4e

shows the unclaimed conventional analog transmission of the intersections of the ultrasonic echo signal (1) of the ultrasonic received signal with the threshold signal (SW).

Figure 4f

shows the unclaimed transmission of analyzed data after complete reception of the ultrasonic echo.

Figure 4g

shows the claimed transmission of compressed data, symbols for signal basic objects being transmitted largely without compression in this example.

Figure 5h

shows the claimed transmission of compressed data, symbols for signal basic objects being compressed into symbols for signal objects in this example. First, a first triangular object (59) identified by the characteristic time sequence of exceeding a threshold value followed by a maximum and falling below a threshold value is recognized and transmitted. Then a double peak with saddle point (60) above the threshold signal is detected. Characteristic here is the sequence in which the ultrasonic echo signal (1) exceeds the threshold signal (SW), followed by a maximum of the ultrasonic echo signal (1), followed by a minimum above the threshold signal (SW), followed by a maximum above of the threshold signal (SW) followed by falling below the threshold signal (SW). After detection, the symbol for this double peak with saddle point is transmitted. A time stamp is also transmitted. Other parameters of the double peak with saddle point are preferably also transmitted, such as the positions of the maxima and the minimum or a scaling factor. A triangular signal is then recognized again as the signal base object as the threshold value signal (SW) being exceeded by the ultrasonic echo signal (1), followed by a maximum of the ultrasonic echo signal (1) followed by the threshold value signal (SW) being undershot the ultrasonic echo signal (1). A double peak is then recognized again, but now the minimum of the ultrasonic echo signal (1) is below the threshold value signal (SW). This double peak can therefore be treated as a separate signal object, for example, if necessary. As can easily be seen, this treatment of the signal leads to a massive data reduction.

figure 6

shows the claimed transmission of compressed data accordingly 3 , whereby in this example not only the envelope signal (ultrasonic echo signal (1)) but also the confidence signal is evaluated.

figure 7

• • Integratoren und/oder
• • Differenzierer und/oder
• • Filter und/oder
• • Logarithmierer und/oder
• • FF- und DFFT-Vorrichtungen und/oder
• • Korrelatoren und/oder
• • Demodulatoren, die ihr Eingangssignal mit einem vorgegebenen Signal multiplizieren und dann filtern, und/oder
• • andere signalverarbeitende Teilvorrichtungen und/oder
• • deren Kombinationen

je nach Anwendungsfall eingesetzt werden, die dann das n-dimensionale Zwischenparametersignal (123) erzeugen. Die mit „Optimalfilter“ bezeichneten Blöcke in den Zeichnungen sind insofern nur als Platzhalter für solche Signalverarbeitungsblöcke zu verstehen. Auch kann ein solcher mit „Optimalfilter“ bezeichneter Signalverarbeitungsblock auch mehr als einen Ausgang haben der zu dem n-dimensionalen Zwischenparametersignal (123) also mehrfach mit mehr als einem Signal beiträgt. Ein nachfolgender, beispielhafter Signifikanzsteigerer dient dazu, den n-dimensionalen Raum des Zwischenparametersignals (123) auf einen m-dimensionalen Raum des Feature-Vektor-Signals (138) abzubilden. Dabei ist m eine ganze positive Zahl. In der Regel ist m kleiner als n. Dies dient zum einen dazu, dass die Selektivität der Parameterwerte, aus denen sich jeder Feature-Vektor-Signalwert des Feature-Vektor-Signals (138) ergibt, maximiert wird. Dies geschieht bevorzugt durch eine lineare Abbildung mit Hilfe eines im Labor mit statistischen Verfahren bestimmten Offset-Werts, der zu den Werten der Zwischenparametersignal hinzuaddiert wird, und einer so genannten LDA Matrix, mit der der jeweilige Vektor der jeweiligen Abtastwerte der Zwischenparametersignale (123) jeweils zum Feature-Vektor-Signal (138) multipliziert wird. Der Abstandsermittler (112) vergleicht bevorzugt jeden sich so ergebenden Feature-Vektor-Signalwert des Feature-Vektor-Signals (138) mit jedem Signalgrundobjektprototypen der Prototypendatenbank (115). Die Prototypendatenbank (115) enthält hierfür für jeden der Signalgrundobjektprototypen der Prototypendatenbank (115) einen Eintrag mit einem Schwerpunktvektor (z.B. 141, 142, 143, 144) des jeweiligen Signalgrundobjektprototypen der Prototypendatenbank (115). Bevorzugt wird ein Abstand des aktuell untersuchten Feature-Vektor-Signalwerts des Feature-Vektor-Signals (138) zu dem geradeuntersuchten Schwerpunktvektor der Prototypendatenbank (115) durch den Abstandsermittler (112) berechnet. Der Abstandsermittler (112) kann aber auch auf andere Weise eine Bewertung der Ähnlichkeit zwischen dem Schwerpunktvektor (z.B. 141, 142, 143, 144) des jeweiligen Signalgrundobjektprototypen der Prototypendatenbank (115) und dem aktuell untersuchten Feature-Vektor-Signalwert des Feature-Vektor-Signals (138) in Form eines Bewertungswerts, den wir hier der Einfachheit halber stets Abstand nennen, berechnen. Der Abstandsermittler (112) ermittelt auf diese Weise ob ein Schwerpunktvektor (z.B. 141, 142, 143, 144) der Signalgrundobjektprototypen der Prototypendatenbank (115) dem aktuellen Feature-Vektor-Signalwert des Feature-Vektor-Signals (138) ausreichend ähnlich ist, also einen ausreichend geringen Abstand hat und, falls dies der Fall ist, welcher Schwerpunktvektor (z.B. 141, 142, 143, 144) der Signalgrundobjektprototypen der Prototypendatenbank (115) dem aktuellen Feature-Vektor-Signalwert des Feature-Vektor-Signals (138) am ähnlichsten ist, also den geringsten Abstand hat. Ggf. ermittelt der Abstandsermittler (112) eine Liste von Schwerpunktvektoren (z.B. 141, 142, d143, 144) der Signalgrundobjektprototypen der Prototypendatenbank (115), die dem aktuellen Feature-Vektor-Signalwert des Feature-Vektor-Signals (138) ausreichend ähnlich sind, also einen ausreichend geringen Abstand haben. Bevorzugt werden diese nach Abstand geordnet und mit ihrem zugehörigen Abstand als Hypothesenliste an den Viterbi-Schätzer weitergegeben. Die Weitergabe des Erkennungsergebnisses des Abstandsermittlers (der hier auch als Klassifikator bezeichnet wird) erfolgt im Falle eines einzelnen erkannten Signalgrundobjekts (121) als Symbol (Z.B. als Prototypendatenbankadresse, die auf den erkannten Signalgrundobjektprototypen der Prototypendatenbank (115) zeigt.) und im Falle einer Hypothesenliste bevorzugt als Liste von Paaren aus einem Symbol des erkannten Signalgrundobjekts (Z.B. der Prototypendatenbankadresse, die auf den erkannten Signalgrundobjektprototypen der Prototypendatenbank (115) zeigt.) und des Abstands zum Schwerpunkt dieses erkannten Signalgrundobjekts. Der Viterbi-Schätzer sucht dann, die Folge aus Signalgrundobjektprototypen, die einer vordefinierten Abfolge von Signalgrundobjektprototypen in seiner Signalobjektdatenbank (116) am besten entspricht. Hierbei werden Übereinstimmungen beispielsweise positiv gezählt und Nichtübereinstimmungen negativ gezählt, sodass sich für eine zeitliche Folge von erkannten Signalgrundobjektprototypen ein Bewertungswert für jeden Eintrag der Signalobjektdatenbank (116) ergibt. Im Falle von Hypothesenlisten überprüft der Viterbi-Schätzer bevorzugt alle möglichen Pfade durch die zeitliche Sequenz von Hypothesenlisten. Bevorzugt ermittelt er sein Bewertungsergebnis unter Berücksichtigung der zuvor ermittelten Abstände. Dies kann beispielsweise durch Division der hinzuaddierten Werte durch den Abstand vor der Addition bei der Bildung des Bewertungswerts geschehen. Auf diese Weise ermittelt der Viterbi-Schätzer die Erkannten Signalobjekte (122), die dann ggf. mit geeigneten Parametern versehen über den Datenbus übertragen werden. Wichtig ist: Es geht bei dieser Vorgehensweise nicht um das Erkennen von physikalisch im Messraum des Ultraschallwandlers (100) vorhandene Objekte und das Übertragen dieser Information, sondern um das Erkennen von Strukturen innerhalb des Ultraschall-Echo-Signals (1) und deren Übertragung.shows an exemplary device in the form of an ultrasonic sensor with detection of signal objects, the data interface, the data bus and the control unit are not shown for the sake of simplicity. The ultrasonic transducer (100) is controlled and measured by means of an ultrasonic transducer signal (102) through a physical interface (101). The physical interface (101) serves to drive the ultrasonic converter (100) and to process the ultrasonic converter signals (102) received from the ultrasonic converter (100) into the ultrasonic echo signal (1) for the subsequent signal object classification. The ultrasonic echo signal (1) is preferably a digitized signal with sampled values spaced apart in time. The feature vector extractor (111) has various devices, here for example n optimal filters (optimal filter 1 to optimal filter n) with n as a positive integer. The outputs of the optimal filters exemplified here form the intermediate parameter signal (123). Instead of the optimal filters or in addition to them, you can also

• • integrators and/or
• • Differentiator and/or
• • Filters and/or
• • Logarithmic converter and/or
• • FF and DFFT devices and/or
• • Correlators and/or
• • Demodulators that multiply their input signal by a given signal and then filter it, and/or
• • other signal processing sub-devices and/or
• • their combinations

are used depending on the application, which then generate the n-dimensional intermediate parameter signal (123). In this respect, the blocks labeled “optimal filter” in the drawings are only to be understood as placeholders for such signal processing blocks. Such a signal processing block labeled “optimal filter” can also have more than one output, which thus contributes to the n-dimensional intermediate parameter signal (123) multiple times with more than one signal. A subsequent, exemplary significance enhancer serves to map the n-dimensional space of the intermediate parameter signal (123) onto an m-dimensional space of the feature vector signal (138). where m is a positive integer. As a rule, m is smaller than n. On the one hand, this serves to maximize the selectivity of the parameter values from which each feature vector signal value of the feature vector signal (138) results. This is preferably done by linear mapping using an offset value determined in the laboratory using statistical methods, which is added to the values of the intermediate parameter signals, and a so-called LDA matrix, with which the respective vector of the respective sample values of the intermediate parameter signals (123) is calculated is multiplied to the feature vector signal (138). The distance determiner (112) preferably compares each resulting feature vector signal value of the feature vector signal (138) with each signal basic object prototype in the prototype database (115). For this purpose, the prototype database (115) contains an entry for each of the basic signal object prototypes in the prototype database (115) with a focus vector (eg 141, 142, 143, 144) of the respective basic signal object prototype in the prototype database (115). A distance between the currently examined feature vector signal value of the feature vector signal (138) and the focus vector of the prototype database (115) just examined is calculated by the distance determiner (112). However, the distance determiner (112) can also evaluate the similarity between the center of gravity vector (e.g. 141, 142, 143, 144) of the respective signal basic object prototype in the prototype database (115) and the currently examined feature vector signal value of the feature vector signal (138) in the form of an evaluation value, which we always call distance here for the sake of simplicity. In this way, the distance detector (112) determines whether a focus vector (e.g. 141, 142, 143, 144) of the signal basic object prototypes in the prototype database (115) is sufficiently similar to the current feature vector signal value of the feature vector signal (138), i.e has a sufficiently small distance and, if this is the case, which centroid vector (e.g. 141, 142, 143, 144) of the signal basic object prototypes of the prototype database (115) most closely resembles the current feature vector signal value of the feature vector signal (138). is, i.e. has the smallest distance. If necessary, the distance finder (112) determines a list of focus vectors (e.g. 141, 142, d143, 144) of the signal basic object prototypes in the prototype database (115) that are sufficiently similar to the current feature vector signal value of the feature vector signal (138). , i.e. have a sufficiently small distance. These are preferably sorted by distance and forwarded to the Viterbi estimator as a list of hypotheses with their associated distance. The transfer of the recognition result of the distance detector (which is also referred to as a classifier here) takes place in the case of a single recognized basic signal object (121) as a symbol (e.g. as a prototype database address that points to the recognized basic signal object prototypes of the prototype database (115)) and in the case of a hypothesis list preferably as a list of pairs of a recognized signal primitive object symbol (eg, the prototype database address pointing to the recognized signal primitive object prototypes of the prototype database (115)) and the distance to the centroid of that recognized signal primitive object. The Viterbi estimator then searches for the sequence of signal basic object prototypes that best corresponds to a predefined sequence of signal basic object prototypes in its signal object database (116). Here, for example, matches are counted positively and non-matches are counted negatively, so that an evaluation value results for each entry in the signal object database (116) for a chronological sequence of recognized basic signal object prototypes. In the case of hypothesis lists, the Viterbi estimator preferably checks all possible paths through the time sequence of hypothesis lists. He preferably determines his evaluation result taking into account the previously determined distances. This can be done, for example, by dividing the added values by the distance before addition when forming the evaluation value. In this way, the Viterbi estimator determines the recognized signal objects (122), which are then optionally provided with suitable parameters and transmitted via the data bus. What is important is that this procedure is not about detecting objects that are physically present in the measurement space of the ultrasonic transducer (100) and transmitting this information, but rather about detecting structures within the ultrasonic echo signal (1) and transmitting them.

figure 8

serves to explain the selection of the basic signal object prototypes in the prototype database (115) by the distance detector (112).

The position of the by the distance determination (112) in the exemplary two-dimensional parameter space 8th determined current feature vector signal values of the feature vector signal (138) can be very different. So it is conceivable that such a first exemplary feature vector signal value (146) is too far away from the centroid coordinates (141, 142, 143, 144) of the centroid of any signal basic object prototype of the prototype database (115). This distance threshold can be, for example, said minimum half prototype distance among the signal basic object prototypes of the prototype database (115). It may also be the case that the scattering ranges of signal basic object prototypes in the prototype database (115) overlap around their respective focal points (143, 142) and a second exemplary determined current feature vector signal value (145) of the feature vector signal (138) in lies in such an overlapping area. In this case, a hypothesis list can contain both signal primitive object prototypes with different probabilities as an attached parameter because of different distances. These probabilities are preferably represented by the distances. A basic signal object is then not transferred to the Viterbi estimator (113) as the most probable one, but rather a vector of basic signal objects that may be present is transferred to the Viterbi estimator (113). From the chronological sequence of these hypothesis lists, the Viterbi estimator then searches for the possible sequence that has the greatest probability of one of the predetermined signal basic object sequences in its signal object database compared to all possible paths through the signal basic objects (121) recognized as possible by the distance detector (112). hypotheses lists received from the Viterbi estimator (113). In such a path, exactly one recognized signal basic object prototype of this hypothesis list must be run through this path for each hypothesis list.

In the best case, the current feature vector signal value (148) is in the scatter range (threshold ellipsoid) (147) around the center of gravity (141) of a single signal basic object prototype (141), which is thus reliably recognized by the distance detector (112) and recognized as a recognized signal basic object (121 ) is passed to the Viterbi estimator (113).

It is conceivable, for improved modeling of the scattering range of a single signal basic object prototype, to model this by means of a plurality of signal basic object prototypes, which are circular in this case, with associated scattering ranges. It is therefore possible for a number of signal basic object prototypes in the prototype database (115) to represent the same signal basic object prototype in the understanding of a signal basic object class.

The distance determiner (112) preferably compares each resulting feature vector signal value of the feature vector signal (138) with each signal basic object prototype in the prototype database (115). For this purpose, the prototype database (115) contains an entry for each of the basic signal object prototypes in the prototype database (115) with a focus vector (eg 141, 142, 143, 144) of the respective basic signal object prototype in the prototype database (115). A distance between the currently examined feature vector signal value of the feature vector signal (138) and the focus vector of the prototype database (115) just examined is calculated by the distance determiner (112). However, the distance determiner (112) can also evaluate the similarity between the center of gravity vector (e.g. 141, 142, 143, 144) of the respective signal basic object prototype in the prototype database (115) and the currently examined feature vector signal value of the feature vector signal (138) in the form of an evaluation value, which we always call distance here for the sake of simplicity. In this way, the distance detector (112) determines whether a focus vector (e.g. 141, 142, 143, 144) of the signal basic object prototypes in the prototype database (115) is sufficiently similar to the current feature vector signal value of the feature vector signal (138), i.e has a sufficiently small distance and, if so, which centroid vector (eg 141, 142, 143, 144) of the signal fundamental object prototypes of the Prototype database (115) is most similar to the current feature vector signal value of the feature vector signal (138), ie has the smallest distance. If necessary, the distance determiner (112) determines a list of focus vectors (e.g. 141, 142, 143, 144) of the signal basic object prototypes in the prototype database (115) that are sufficiently similar to the current feature vector signal value of the feature vector signal (138). , i.e. have a sufficiently small distance.

figure 9

serves to explain the HMM method used by the Viterbi estimator (113) to identify the signal object as the most probable sequence of signal basic object prototypes based on a sequence of recognized signal basic object prototypes (121) as a recognized signal object (122). The 9 is explained in the text above.

figure 10

shows an exemplary state sequence in the Viterbi estimator (113) for the detection of a single signal object. The 10 is explained in the text above.

figure 11

shows an exemplary, preferred state sequence in the Viterbi estimator (113) for the continuous recognition of one of signal objects, as is typically necessary for the recognition of autonomous driving tasks. The 11 is explained in the text above.

figure 12

Figure 1 shows an alternative embodiment with an estimator (151) executing a neural network model. The data interface, the data bus and the control unit are not shown for the sake of simplicity. The ultrasonic transducer (100) is controlled and measured by means of an ultrasonic transducer signal (102) through a physical interface (101). The physical interface (101) serves to drive the ultrasonic converter (100) and to process the ultrasonic converter signal (102) received from the ultrasonic converter (100) into the ultrasonic echo signal (1) for the subsequent signal object classification. The ultrasonic echo signal (1) is preferably a digitized signal with sampled values spaced apart in time. The feature vector extractor (111) again has various devices, here, for example, n optimal filters (optimal filter 1 to optimal filter n) with n as a positive integer. The outputs of the optimal filters exemplified here form the intermediate parameter signal (123). Instead of or in addition to the optimal filters, integrators, filters, differentiators, logarithmic converters and/or other signal-processing sub-devices and combinations thereof can be used, depending on the application, which then generate the n-dimensional intermediate parameter signal (123). A subsequent, exemplary significance enhancer serves to map the n-dimensional space of the intermediate parameter signal (123) onto an m-dimensional space of the feature vector signal (138). where m is a positive integer. As a rule, m is smaller than n. On the one hand, this serves to maximize the selectivity of the parameter values from which each feature vector signal value of the feature vector signal (138) results. This is preferably done by linear mapping using an offset value determined in the laboratory using statistical methods, which is added to the values of the intermediate parameter signals, and a so-called LDA matrix, with which the respective vector of the respective sample values of the intermediate parameter signals (123) is calculated is multiplied to the feature vector signal (138). The estimator (151) then attempts to recognize the signal objects in the data stream of the feature vector signal values of the feature vector signal (138) with the aid of a neural network model (151). Each signal basic object prototype and the signal objects are encoded via the networking of the nodes within the neural network model and the parameterization of the neural network model within the neural network model. The estimator (151) then outputs the recognized signal objects (122).

figure 13

FIG. 12 shows the exemplary decompression of the transmitted exemplary envelope signal (ultrasonic echo signal (1)) of the ultrasonic reception signal limited to the chirp-down signals.

First, an ultrasonic echo signal model is generated that has no signal. ( 13a) . The ultrasound echo signal model is parameterized with a model parameter (SA) which is preferably correlated with the time (t) since the transmission of the ultrasound pulse or burst.

This ultrasonic echo signal model is then preferably suitably parameterized by adding the first exemplary signal object (160) transmitted from the sensor to the control unit with data transmission priority 1, which describes a triangular shape with chirp down (A). ( 13b)

The second exemplary signal object (161) in triangular form with a chirp-up characteristic (B) transmitted with data priority 2 is not taken into account here for the reconstruction of the chirp-down signal.

The third exemplary signal object (162) transmitted with data priority 3 in the form of a double peak with a chirp-up characteristic (B) is not taken into account here for the reconstruction of the chirp-down signal.

The fourth example signal object (163) transmitted from the sensor to the control device with data transmission priority 4, which describes a triangular shape with chirp down (A), is then suitably parameterized, preferably supplemented to the ultrasonic echo signal model that has already been supplemented. ( 13c )

The ultrasonic echo signal model that has already been supplemented is then supplemented, suitably parameterized, preferably by adding the fifth exemplary signal object (164) transmitted from the sensor to the control unit with data transmission priority 5, which describes a triangular shape with chirp down (A). ( 13d )

The sixth exemplary signal object (165), transmitted with data priority 6, in the form of a double peak with a chirp-up characteristic (B) is not taken into account here for the reconstruction of the chirp-down signal.

The resulting reconstructed envelope signal is the reconstructed chirp-down ultrasonic echo signal.

The ultrasonic echo signal decompressed and reconstructed in this way is then typically used for object recognition in the control unit or at another location in the vehicle.

figure 14

FIG. 12 shows the exemplary decompression of the transmitted exemplary envelope signal (ultrasonic echo signal (1)) of the ultrasonic reception signal limited to the chirp-up signals.

First, an ultrasonic echo signal model is generated that has no signal. ( 14a) . The ultrasound echo signal model is parameterized with a model parameter (SA) which is preferably correlated with the time (t) since the transmission of the ultrasound pulse or burst.

The first exemplary signal object (160) in the form of a triangle with a chirp-down characteristic (A) transmitted with data priority 1 is not taken into account here for the reconstruction of the chirp-up signal.

This ultrasonic echo signal model is then preferably suitably parameterized by adding the second exemplary signal object (161) transmitted from the sensor to the control unit with data transmission priority 2, which describes a triangular shape with chirp up (B). ( 14b)

This ultrasonic echo signal model is then preferably suitably parameterized by adding the third exemplary signal object (161) transmitted from the sensor to the control unit with data transmission priority 3, which describes a double peak shape with chirp up (B). ( 14c )

The fourth exemplary signal object (162) in triangular form with a chirp-down characteristic (A) transmitted with data priority 4 is not taken into account here for the reconstruction of the chirp-up signal.

The fifth exemplary signal object (163) in triangular form with a chirp-down characteristic (A) transmitted with data priority 5 is not taken into account here for the reconstruction of the chirp-up signal.

This ultrasonic echo signal model is then preferably suitably parameterized by adding the sixth exemplary signal object (165) transmitted from the sensor to the control unit with data transmission priority 6, which describes a triangular shape with chirp up (B). ( 14d )

The resulting reconstructed envelope signal is the reconstructed chirp-up ultrasonic echo signal.

The ultrasonic echo signal decompressed and reconstructed in this way is then typically used for object recognition in the control unit or at another location in the vehicle.

Figure 15 and 16

show the decompression of the transmitted envelope signal (ultrasonic echo signal (1)) of the ultrasonic reception signal with chirp-up signals and chirp-down signals;

First, an ultrasonic echo signal model is generated that has no signal. ( 15a) . The ultrasound echo signal model is parameterized with a model parameter (SA) which is preferably correlated with the time (t) since the transmission of the ultrasound pulse or burst.

This ultrasonic echo signal model is then preferably suitably parameterized by adding the first exemplary signal object (160) transmitted from the sensor to the control unit with data transmission priority 1, which describes a triangular shape with chirp down (A). ( 15b)

This ultrasonic echo signal model is then preferably suitably parameterized by adding the second exemplary signal object (161) transmitted from the sensor to the control unit with data transmission priority 2, which describes a triangular shape with chirp up (B). ( 15c )

This ultrasonic echo signal model is then preferably suitably parameterized by adding the third exemplary signal object (162) transmitted from the sensor to the control unit with data transmission priority 3, which describes a double peak shape with chirp up (B). ( 15d )

This ultrasonic echo signal model is then preferably suitably parameterized by adding the fourth exemplary signal object (163) transmitted from the sensor to the control unit with data transmission priority 4, which describes a triangular shape with chirp down (A). ( 16d )

This ultrasonic echo signal model is then preferably suitably parameterized by adding the fifth exemplary signal object (164) transmitted from the sensor to the control unit with data transmission priority 5, which describes a triangular shape with chirp down (A). ( 16f)

This ultrasonic echo signal model is then preferably suitably parameterized by adding the sixth exemplary signal object (165) transmitted from the sensor to the control unit with data transmission priority 6, which describes a triangular shape with chirp up (B). ( 16g)

For clarification is in 4 p.m the reconstructed envelope signal is drawn in bold. Otherwise correct 4 p.m with the 16g match.

The resulting reconstructed envelope signal is the reconstructed chirp-up/chirp-down ultrasonic echo signal.

The ultrasonic echo signal decompressed and reconstructed in this way is then typically used for object recognition in the control unit or at another location in the vehicle.

figure 17

shows the original signal (a), the reconstructed signal (b) and their superimposition (c). The deviations between the reconstructed ultrasonic echo signal (166) and the ultrasonic echo signal are only very small if the procedure is correct and the basic signal objects and signal objects are chosen skilfully.

figure 18

18 shows an improved device for improved compression. The in the 13 until 17 The reconstructor (600) explained in terms of its function can be used not only in the control unit, but also in the sensor itself prior to data transmission. For this purpose, the reconstructor (600) generates a reconstructed ultrasonic echo signal model (610) as shown in FIGS 13 until 17 presented according to purpose. This is subtracted from the previously received ultrasonic echo signal (1) in a subtractor (602). For this purpose, the ultrasonic echo signal (1) is stored preferably in the form of digital sampled values in a memory (601), preferably ordered in terms of time. A stored sample value of the ultrasonic echo signal (1) preferably corresponds to exactly one value of the reconstructed ultrasonic echo signal model (610), which is preferably also stored in a reconstruction memory (603). The reconstruction memory (603) can be part of the reconstructor (600). This additional feedback branch (600, 610, 603, 602, 601) can also be used for other classifiers such as the device in FIG 12 be used.

Reference List

a

sending out the ultrasonic burst;

β

receiving the ultrasonic burst reflected from an object and converting it into a received electrical signal;

g

compression of the received electrical signal;

yes

Sampling of the electrical input signal and formation of a sampled electrical input signal, it being possible for a time stamp to be assigned to each sampled value of the electrical input signal.

γb

Determination of several spectral values, e.g. by means of optimal filters (English matched filters) for prototypical signal object classes. These multiple spectral values together form a feature vector. This formation preferably takes place continuously, resulting in a stream of feature vector values. A time stamp value can preferably be assigned again to each feature vector value;

γc

optional but preferably performed normalization of the feature vector spectral coefficients of the respective feature vector of a time stamp value before correlation with the prototypical signal object classes in the form of predefined prototypical feature vector values of a prototype library;

γd

determining the distance between the current feature vector value and the values of the prototypical signal object classes in the form of predefined prototypical feature vector values of a prototype library;

γe

Selection of the most similar prototypical signal object class in the form of a predefined prototypical feature vector value of a prototype library with preferably minimal distance to the current feature vector and taking over the symbol of this signal object class as recognized signal object together with the time stamp value as compressed data. If necessary, further data, in particular signal object parameters, such as its amplitude, can also be taken over as compressed data. This compressed data then forms the compressed received signal;

δ

Transmission of the compressed electrical reception signal to the computer system:

1

Envelope curve of the received ultrasonic signal, also referred to here as the ultrasonic echo signal;

2

Output signal (transmitted information) of an IO interface according to the prior art;

3

transmitted information of a LIN interface according to the prior art;

4

Exemplary, first point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction.;

5

exemplary, first point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction;

6

exemplary first maximum of the ultrasonic echo signal (1) above the threshold signal (SW);

7

exemplary, second point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction;

8th

exemplary, second point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction;

9

exemplary second maximum of the ultrasonic echo signal (1) above the threshold value signal (SW);

10

exemplary first minimum of the ultrasonic echo signal (1) above the threshold signal (SW);

11

exemplary third maximum of the ultrasonic echo signal (1) above the threshold value signal (SW);

12

exemplary, third point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction;

13

exemplary, third point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction;

14

exemplary fourth maximum of the ultrasonic echo signal (1) above the threshold value signal (SW);

15

exemplary, fourth point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction;

16

exemplary, fourth point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction;

17

exemplary fifth maximum of the ultrasonic echo signal (1) above the threshold signal (SW);

18

exemplary, fifth point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction;

19

exemplary, fifth point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction;

20

exemplary sixth maximum of the ultrasonic echo signal (1) above the threshold value signal (SW);

21

exemplary, sixth point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction;

22

exemplary, sixth point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction;

23

exemplary seventh maximum of the ultrasonic echo signal (1) above the threshold signal (SW);

24

exemplary, seventh point of intersection of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction;

25

Envelope curve during the ultrasonic burst

26

Transmission of the data of the exemplary, first point of intersection (4) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction via the preferably bidirectional data bus;

27

Transmission of the data of the exemplary, first point of intersection (5) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction via the preferably bidirectional data bus;

28

Transmission of the data of the exemplary first maximum (6) of the ultrasonic echo signal (1) above the threshold value signal (SW) via the preferably bidirectional data bus;

29

Transmission of the data of the exemplary, second point of intersection (7) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction via the preferably bidirectional data bus;

30

Transmission of the data of the exemplary, second point of intersection (8) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction via the preferably bidirectional data bus;

31

Transmission of the data of the exemplary second maximum (9) of the ultrasonic echo signal (1) above the threshold signal (SW) and the data of the exemplary first minimum (10) of the ultrasonic echo signal (1) above the threshold signal (SW) via the preferably bidirectional data bus;

32

Transmission of the data of the exemplary third maximum (11) of the ultrasonic echo signal (1) above the threshold value signal (SW) via the preferably bidirectional data bus;

34

Transmission of the data of the exemplary, third point of intersection (12) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction via the preferably bidirectional data bus;

35

Transmission of the data of the exemplary, third point of intersection (13) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction via the preferably bidirectional data bus;

36

Transmission of the data of the exemplary fourth maximum (14) of the ultrasonic echo signal (1) above the threshold value signal (SW) via the preferably bidirectional data bus;

37

Transmission of the data of the exemplary, fourth point of intersection (15) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction via the preferably bidirectional data bus;

38

Transmission of the data of the exemplary, fourth point of intersection (16) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction via the preferably bidirectional data bus;

39

Transmission of the data of the exemplary, fifth maximum (17) of the ultrasonic echo signal (1) above the threshold value signal (SW) via the preferably bidirectional data bus;

40

Transmission of the data of the exemplary, fifth point of intersection (18) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction via the preferably bidirectional data bus;

41

Transmission of the data of the exemplary, fifth point of intersection (19) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the upward direction via the preferably bidirectional data bus;

42

Transmission of the data of the exemplary, sixth point of intersection (21) of the ultrasonic echo signal (1) with the threshold value signal (SW) in the downward direction via the preferably bidirectional data bus;

43

transmission of the data of the received echoes on the LIN bus according to the prior art after the end of the reception;

44

Prior art transmission of data on the LIN bus prior to transmission of the ultrasonic burst;

45

Transmission of data via the IO interface according to the prior art before transmission of the ultrasonic burst;

46

Effect of the ultrasonic transmit burst on the output signal of the prior art IO interface;

47

Signal of the first echo (5, 6, 7) on the IO interface according to the prior art;

48

signal of the second echo (8, 9, 10, 11, 12) on the IO interface according to the prior art;

49

signal of the third and fourth echoes (13, 14, 15) on the IO interface according to the prior art;

50

signal of the fifth echo (16, 17, 18) on the IO interface according to the prior art;

51

Signal of the sixth echo (19, 20, 21) on the IO interface according to the prior art;

52

signal of the seventh echo (22, 23, 24) on the IO interface according to the prior art;

53

Start command from the computer system to the sensor via the data bus;

54

Periodic automatic data transmission between sensor and computer system, preferably according to the DSI3 standard;

55

diagnostic bits after the measurement cycle;

56

End of transmission of the ultrasonic burst (end of transmission burst). The end of the ultrasonic burst preferably coincides with point 4 .

57

Beginning of the transmission of the ultrasonic burst (beginning of the transmission burst).

58

end of data transfer

100

Ultrasonic transducer, which can also include a separate ultrasonic transmitter and a separate ultrasonic receiver, for example;

101

physical interface for driving the ultrasonic transducer (100) and for processing the ultrasonic transducer signal (102) received from the ultrasonic transducer (100) to form the ultrasonic echo signal (1) for the subsequent signal object classification;

102

ultrasonic transducer signal;

111

feature vector extractor (111);

112

distance finder (or classifier);

113

Viterbi estimator.

115

prototype database;

116

signal object database;

122

recognized signal objects with signal object parameters;

123

intermediate parameter signals;

125

significance enhancer;

126

LDA matrix;

138

feature vector signal or feature vector signal;

141

Centroid coordinate of a first exemplary prototype signal base object of the prototype database (115);

142

Centroid coordinate of a second exemplary prototype signal base object of the prototype database (115);

143

Centroid coordinate of a third exemplary prototype signal base object of the prototype database (115);

144

Centroid coordinate of a fourth exemplary prototype signal base object of the prototype database (115);

145

current feature vector signal value of the feature vector signal (138) in the overlapping area of the scatter areas of the two exemplary signal basic objects of the prototype database (115) with the center of gravity coordinates 142 and 143;

146

exemplary feature vector signal value that is too far from the exemplary centroid coordinates (141, 142, 143, 144) of the centroid of any exemplary signal primitive prototype of the prototype database (115);

147

Scatter (threshold ellipsoid) (147) around the centroid (141) of a single exemplary signal ground object prototype (141);

148

exemplary current feature vector signal value which is in the scatter range (threshold ellipsoid) (147) around the center of gravity (141) of a single exemplary signal basic object prototype (141) and which is thus reliably recognized by the distance detector (112) and recognized as a recognized signal basic object (121). can be passed on to the Viterbi estimator (113).

150

Estimator with an HMM model;

151

estimator with a neural network model;

160

first exemplary signal object in triangle shape with chirp down (A) and data transmission priority 1;

161

second example signal object in triangle shape with chirp up (B) and data transmission priority 2;

162

third exemplary signal object in double peak form with chirp up (B) and data transmission priority 3 (curbstone profile);

163

fourth exemplary signal object in triangle shape with chirp down (A) and data transmission priority 4;

164

fifth exemplary signal object in triangle shape with chirp down (A) and data transmission priority 5;

165

sixth exemplary signal object in triangle shape with chirp UP (B) and data transmission priority 6;

166

echo signal transmitted and decompressed with the aid of signal objects;

600

reconstructor;

601

Memory for the ultrasonic echo signal (1). The memory preferably includes the data of the echo of an ultrasonic pulse or burst;

602

Subtractor for subtracting the reconstructed ultrasonic echo signal samples calculated by the reconstructor (600) forming the reconstructed ultrasonic echo signal model (610) from the ultrasonic echo signal samples stored in the memory (601) signal (1) to form the residual signal (660) which serves as an alternative input signal for the feature vector extractor (111) in place of the ultrasonic echo signal (1);

603

Reconstruction memory for the reconstructed ultrasonic echo signal model (610). The reconstruction store typically runs as part of the reconstructor (600).

610

reconstructed ultrasonic echo signal model;

660

residual signal. The residual signal represents the compression error. The more objects are detected, the smaller the compression error becomes. Compression is typically terminated when all samples of the residual signal (660) are below a compression threshold curve. The corresponding termination signaling is not shown in the figures.

a

Transmitted information for the transmission of the received ultrasonic echoes by means of a prior art IO interface;

A

Example detected object with chirp down; also Arbitrary units = freely chosen units

B

Exemplary detected object with chip-up;

b

Transmitted information for the transmission of the received ultrasonic echoes by means of a prior art LIN interface;

c

Transmitted information for the transmission of the received ultrasonic echoes by means of the proposed method and the proposed device with an envelope (ultrasonic echo signal (1)) for comparison;

i.e

Transmitted information for the transmission of the received ultrasonic echoes by means of the proposed method and device without an envelope;

e

schematic signal forms in the transmission of the received echo information by means of an IO interface from the prior art;

En

amplitude of the envelope (ultrasonic echo signal (1)) of the received ultrasonic signal;

f

schematic signal forms in the transmission of the received echo information by means of a LIN interface from the prior art;

G

schematic signal forms in the transmission of the received echo information by means of a bidirectional data interface;

SA

Memory address in memory (601) or reconstruction memory (603). This is typically correlated with a time since the transmission of an ultrasonic pulse and/or ultrasonic burst.

SB

transmit burst

SW

threshold

t

Time;

TE

reception time. The reception time typically begins with the end (56) of the transmission of the ultrasonic burst. It is possible to start receiving earlier. However, this can lead to problems that may require additional measures.

Claims (29)

Sensor system - with at least one computer system and - with at least one sensor, - the sensor system carrying out a method for transmitting sensor data, in particular an ultrasonic sensor, from the sensor to the computer system, in particular in a vehicle, and - the sensor carrying out a method in Called the following compression method, which has or comprises the following steps: • sending out an ultrasonic burst; • receiving an ultrasonic signal and forming an ultrasonic received signal; • forming an ultrasonic echo signal (1) from the received ultrasonic signal; • Storing the ultrasonic echo signal (1) in the form of digital samples in a memory (601); • subtracting a reconstructed ultrasonic echo signal model (610) stored in a reconstruction memory (603) from the previously received ultrasonic echo signal (1) to form a residual signal (660); • forming a feature vector signal (138) from the residual signal (660); • Evaluation of at least time sections (Hamming windows) of the feature vector signal (138) • by forming at least one binary, digital or analog distance value between the feature vector signal and one or more signal object prototype values for recognizable signal object classes and assigning a recognizable signal object class as a recognized signal object if the absolute value of the distance value falls below one or more predetermined, binary, digital or analog distance values, and/or • by assigning a recognizable signal object class as a recognized signal object, in particular by means of an estimator (151), with a neural network model and/or or with a Petri net; • Recognition of signal objects and classification of these signal objects into assigned signal object classes within the ultrasonic received signal (1), • whereby a signal object class can also include only one signal object and • whereby each thus recognized and classified signal object (122) has at least one associated signal object parameter and a symbol corresponding to this be assigned to a signal object assigned to a signal object class, or • at least one assigned signal object parameter and a symbol for this signal object being determined for each signal object (122) thus recognized and classified; • Generating the reconstructed ultrasonic echo signal model (610) from the summing linearly superimposed signal curve models of the detected signal objects; • Storing the ultrasonic echo signal model (610) in the reconstruction memory (603) • Ending this reducing classification of the ultrasonic echo signal (1) into signal objects using the ultrasonic echo signal model (610), if the magnitudes of the sampled values of the residual signal (660) are below the magnitudes of a predetermined threshold value curve and • transmission of at least the symbol of a recognized signal object class and at least one associated signal object parameter of this recognized signal object class to the computer system and - the computer system using a decompression method for decompression of signal object-oriented to this Performs compressed ultrasound reception data and - the computer system receiving the data from the sensor and - the decompression method comprising the following steps: receiving the data to be decompressed in the form of the transmitted symbols of the recognized signal object classes and the associated signal object parameters of these recognized signal object classes; • providing an ultrasonic echo signal model that has no signal; • Supplementing the ultrasonic echo signal model by adding a prototypical parameterized signal curve of a signal object that is contained in the ultrasonic reception data; • Forming the reconstructed ultrasonic received signal, which can be done in the form of forming samples of a reconstructed envelope; • Using the reconstructed ultrasound reception signal. sensor system claim 1 - wherein within the compression method that the sensor carries out, the transmission of at least the symbol of a recognized signal object class and at least the one associated signal object parameter of this recognized signal object class is prioritized in terms of time based on the chronological transmission order by the sensor of the symbols of the signal object classes recognized within the sensor and at least of the these respectively assigned signal object parameters of these signal object classes recognized within the sensor takes place from the sensor to the computer system in a point-to-point connection. Sensor system according to one or more of Claims 1 until 2 , - wherein the compression method performed by the sensor comprises the following additional step: - determining a chirp value as associated signal object parameter, which indicates whether the detected signal object is an echo of an ultrasonic transmission burst with chirp-up or a chirp down or a no chirp property. Sensor system according to one or more of Claims 1 until 3 - where the classification is done with the help of a neural network model. Sensor system according to one or more of Claims 1 until 4 - where the classification is done using a Hidden Markov Model (HMM). Sensor system according to one or more of Claims 1 until 4 , - the compression method carried out by the sensor comprising the following additional step: - generating a confidence signal by forming a correlation between the received signal or a signal derived therefrom on the one hand and a reference signal on the other hand. Sensor system according to one or more of Claims 1 until 6 , - the compression method performed by the sensor comprising the following additional step: - generating a phase signal. sensor system claim 7 , - the compression method carried out by the sensor comprising the following additional step: - generating a phase confidence signal by forming the correlation between the phase signal or a signal derived therefrom on the one hand and a reference signal. sensor system claim 8 , - wherein the compression method performed by the sensor comprises the following additional step: - comparing the phase confidence signal with one or more threshold values to generate a discretized phase confidence signal. Sensor system according to one or more of Claims 1 until 9 - where at least one signal object class is a wavelet. sensor system claim 10 - wherein the at least one wavelet is a triangular wavelet. Sensor system according to one or more of Claims 10 until 11 - wherein the at least one wavelet is a rectangular wavelet. Sensor system according to one or more of Claims 10 until 12 - wherein the at least one wavelet is a half-sine wavelet. Sensor system according to one or more of Claims 10 until 13 - where one of the signal object parameters • is a time shift of the wavelet of the detected signal object or • is a temporal compression or expansion of the wavelet of the detected signal object or • is an amplitude of the wavelet of the detected signal object. Sensor system according to one or more of Claims 1 until 14 - wherein the transmission of error states of the sensor • takes place with higher priority than the transmission of the at least one recognized signal object class and/or • compared to the transmission of the one assigned signal object parameter. Sensor system according to one or more of Claims 1 until 15 - wherein a signal object comprises the combination of two or three or four or more signal basic objects. sensor system Claim 16 - wherein a signal fundamental object is the crossing of the magnitude of the amplitude of the envelope signal (1) with the magnitude of a threshold signal (SW) at a crossing instant. Sensor system according to one or more of Claims 16 until 17 - wherein a signal fundamental object is the crossing of the magnitude of the amplitude of the envelope signal (1) with the magnitude of a threshold signal (SW) at a crossing instant in the ascending direction. Sensor system according to one or more of Claims 16 until 18 - wherein a signal fundamental object is the crossing of the magnitude of the amplitude of the envelope signal (1) with the magnitude of a threshold signal (SW) at a crossing instant in the descending direction. Sensor system according to one or more of Claims 16 until 19 - wherein a signal fundamental object is a maximum of the magnitude of the amplitude of the envelope signal (1) above the magnitude of a threshold signal (SW) at a maximum point in time. Sensor system according to one or more of Claims 16 until 20 - wherein a signal fundamental object is a minimum of the magnitude of the amplitude of the envelope signal (1) above the magnitude of a threshold signal (SW) at a minimum instant. Sensor system according to one or more of Claims 16 until 21 - where a basic signal object is a predefined chronological sequence and/or chronological grouping of other basic signal objects. Sensor system according to one or more of Claims 1 until 22 - wherein the transmission of at least the symbol of a recognized signal object class and at least one associated signal object parameter of this recognized signal object class is the transmission of the signal object class of a signal object that is a predefined chronological sequence of other signal objects, and wherein at least one signal object class at least one of these other signal objects is not transmitted . Sensor system according to one or more of Claims 1 until 23 - Wherein the decompression method comprises the following step: - Using the reconstructed ultrasound reception signal in a vehicle for the purpose of autonomous driving and/or for the purpose of generating the environment map. Sensor system according to one or more of Claims 1 until 24 - With at least two sensors that execute the compression method, - the sensor system being provided for the purpose that the data transmission between the sensors and the computer system corresponds to one or more of the Claims 1 until 24 expires or may expire. sensor system Claim 25 - Wherein within the sensors in each case an ultrasonic reception signal, ie at least two ultrasonic reception signals, according to the compression method according to one or more of Claims 1 until 25 is compressed and transmitted to the computer system and - wherein within the computer system, the at least two ultrasound reception signals by decompression sion of data received from the sensor can be reconstructed into reconstructed ultrasound reception signals. sensor system Claim 25 or Claim 26 - Wherein the computer system carries out object recognition of objects in the vicinity of the sensors with the aid of reconstructed ultrasound reception signals. sensor system Claim 27 - The computer system using reconstructed ultrasound reception signals and additional signals from other sensors, in particular signals from radar sensors, carries out an object recognition of objects in the vicinity of the sensors. Sensor system according to one or more of claims 27 or 28 - The computer system based on the detected objects creates a map of the environment for the sensors or a device, part of which are the sensors.

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