JP7244220B2 - Radar device and target data output method - Google Patents

Description

The present invention relates to a radar device and a target object data output method.

2. Description of the Related Art Conventionally, for example, there is a radar apparatus that is mounted on a vehicle and detects a target based on a reflected wave that is reflected by the target from a transmission wave that is transmitted from the vehicle. Such a radar device generates one representative data representative of the target from instantaneous data corresponding to the same target, and outputs the representative data (see, for example, Patent Document 1).

JP 2015-210157 A

However, in the conventional technology, even when a plurality of instantaneous data are detected from the same target, only one representative data is output, so the shape of the target cannot be correctly recognized from the output representative data. .

SUMMARY OF THE INVENTION It is an object of the present invention to provide a radar device and a target data output method that can correctly recognize the shape of a target.

In order to solve the above-described problems and achieve the object, a radar device according to an embodiment includes a generation section, a filtering section, and an output section. The generator generates instantaneous data corresponding to the target based on a reflected wave of the transmitted wave reflected by the target. A filter processor generates representative data for each of the targets based on the instantaneous data generated by the generator. The output unit outputs output data obtained by adding shape data of the target based on the instantaneous data used to generate the representative data to the representative data generated by the filtering unit.

ADVANTAGE OF THE INVENTION According to this invention, the shape of a target can be correctly recognized.

FIG. 1A is a diagram showing a mounting example of the radar device 1. FIG. FIG. 1B is a diagram showing an outline of a target object data output method. FIG. 2 is a block diagram of the radar device. FIG. 3 is a process explanatory diagram from pre-processing of the signal processing unit to peak extraction processing in the signal processing unit. FIG. 4A is a process explanatory diagram of the angle estimation process. FIG. 4B is a process explanatory diagram (part 1) of the pairing process. FIG. 4C is a process explanatory diagram (part 2) of the pairing process. FIG. 5A is a diagram (part 1) showing a specific example of output data. FIG. 5B is a diagram (part 2) showing a specific example of output data. FIG. 5C is a diagram (part 3) showing a specific example of output data. FIG. 6 is a flow chart showing a processing procedure executed by the radar device. FIG. 7 is a diagram showing a specific example of output data according to the modification.

Embodiments of a radar device and a target data output method disclosed in the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the present invention is not limited by this embodiment. In the following description, a case where the radar system is of the FM-CW (Frequency Modulated Continuous Wave) system will be described as an example, but the radar system may be of the FCM (Fast-Chirp Modulation) system.

First, the outline of the target data output method according to the embodiment will be described with reference to FIGS. 1A and 1B. FIG. 1A is a diagram showing an example of mounting a radar device. FIG. 1B is a diagram showing an outline of target data output. FIG. 1A shows an own vehicle MC equipped with a radar device 1 according to the embodiment, and another vehicle LC located in front of the own vehicle MC.

As shown in FIG. 1A, the radar device 1 is mounted, for example, in the front grille of the own vehicle MC, and detects targets (for example, other vehicles LC, etc.) existing in the traveling direction of the own vehicle MC. Note that the radar device 1 may be mounted at other locations such as a windshield, a rear grille, and left and right side portions (for example, left and right door mirrors).

Further, as shown in FIG. 1A, the radar device 1 generates instantaneous data 100 corresponding to the target based on the reflected waves of the radio waves transmitted around the host vehicle MC reflected by the other vehicle LC. The instantaneous data 100 includes information such as the relative speed toward the own vehicle MC, the distance between the own vehicle MC and the instantaneous data 100, and the direction of the instantaneous data 100. FIG.

The radar device 1 also performs time-series filtering on the generated instantaneous data 100 to generate target data corresponding to the target as a filter value. This makes it possible to follow (track) the target data.

After that, the radar device 1 performs grouping to integrate a plurality of target data into one as target data of the same object, and generates representative data 50 . For example, the radar device 1 regards target data whose detected positions and velocities are close within a predetermined range as target data of the same object, and generates one piece of representative data 50 from such target data.

However, even if multiple target data are detected, only one representative data 50 is output for one target. Therefore, the shape of the target cannot be recognized from the representative data 50 .

Therefore, in the target data output method according to the embodiment, the shape of the target is correctly recognized by outputting the output data Do obtained by adding the shape data related to the shape of the target to the representative data 50 .

Specifically, as shown in FIG. 1B, the output data Do includes, in addition to the representative data 50, instantaneous data 100 used to generate the representative data 50. FIG. That is, in the target data output method according to the embodiment, the instantaneous data 100 used to generate the representative data 50 is reused as target shape data.

As described above, in the target data output method according to the embodiment, by adding the instantaneous data 100 to the representative data 50 as the shape data representing the shape of the object, it is possible to correctly recognize the shape of the target. Note that the instantaneous data 100 is an example of shape data, and may be other data as long as it indicates a shape.

Next, with reference to FIG. 2, the configuration of the radar device 1 according to the embodiment will be described in detail. FIG. 2 is a block diagram showing the configuration of the radar device 1 according to the embodiment. In addition, in FIG. 2, only the components necessary for explaining the features of the present embodiment are represented by functional blocks, and the description of general components is omitted.

As shown in FIG. 2 , the radar device 1 includes a transmitter 10 , a receiver 20 and a processor 30 . The radar device 1 is connected to a vehicle control device 2 that controls the behavior of the own vehicle MC.

The vehicle control device 2 performs vehicle control such as PCS (Pre-crash Safety System) and AEB (Advanced Emergency Braking System) based on the target detection result by the radar device 1 .

The transmitter 10 includes a signal generator 11 , an oscillator 12 and a transmission antenna 13 . The signal generation unit 11 generates a modulation signal for transmitting millimeter waves frequency-modulated with a triangular wave under the control of the transmission/reception control unit 31, which will be described later. The oscillator 12 generates a transmission signal based on the modulated signal generated by the signal generator 11 and outputs it to the transmission antenna 13 . Incidentally, as shown in FIG. 2, the transmission signal generated by the oscillator 12 is also distributed to the mixer 22, which will be described later.

The transmission antenna 13 converts a transmission signal from the oscillator 12 into a transmission wave, and outputs the transmission wave to the outside of the own vehicle MC. A transmission wave output from the transmission antenna 13 is a continuous wave frequency-modulated with a triangular wave. A transmission wave transmitted from the transmission antenna 13 to the outside of the own vehicle MC, for example, to the front is reflected by a target such as another vehicle LC and becomes a reflected wave.

The receiving section 20 includes a plurality of receiving antennas 21 forming an array antenna, a plurality of mixers 22 and a plurality of A/D converting sections 23 . Mixer 22 and A/D converter 23 are provided for each receiving antenna 21 .

Each receiving antenna 21 receives a reflected wave from a target as a received wave, converts the received wave into a received signal, and outputs the received signal to the mixer 22 . Although the number of receiving antennas 21 shown in FIG. 2 is four, the number may be three or less or five or more.

A received signal output from the receiving antenna 21 is input to the mixer 22 after being amplified by an amplifier (for example, a low noise amplifier) not shown. The mixer 22 mixes the distributed transmission signal and part of the received signal input from the receiving antenna 21 to remove unnecessary signal components, generates a beat signal, and outputs the beat signal to the A/D converter 23 . .

The beat signal has a beat frequency that is the difference between the frequency of the transmission signal (hereinafter referred to as "transmission frequency") and the frequency of the reception signal (hereinafter referred to as "reception frequency"). The beat signal generated by the mixer 22 is synchronized in timing between the receiving antennas 21 by a synchronization section (not shown), converted into a digital signal by the A/D conversion section 23 , and then output to the processing section 30 .

The processing unit 30 includes a transmission/reception control unit 31 , a signal processing unit 32 and a storage unit 36 . The signal processor 32 includes a generator 33 and a filter processor 34 .

The storage unit 36 stores history data 36a. The history data 36 a is information including the history of target data in a series of signal processing executed by the signal processing unit 32 and the history of the instantaneous data 100 .

The processing unit 30 is, for example, a microcomputer including a CPU (Central Processing Unit), ROM (Read Only Memory) and RAM (Random Access Memory) corresponding to the storage unit 36, registers, other input/output ports, and the like. It controls the entire device 1 .

The CPU of such a microcomputer functions as a transmission/reception control section 31 and a signal processing section 32 by reading out and executing programs stored in the ROM. The transmission/reception control unit 31 and the signal processing unit 32 can also be configured entirely by hardware such as ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

The transmission/reception control section 31 controls the transmission section 10 including the signal generation section 11 and the reception section 20 . The signal processing unit 32 periodically executes a series of signal processing. Next, each component of the signal processing unit 32 will be described.

The generator 33 generates instantaneous data 100 . Specifically, the generator 33 generates the instantaneous data 100 by performing frequency analysis processing, peak extraction processing, and instantaneous data generation processing.

In the frequency analysis process, the beat signal input from each A/D converter 23 is subjected to Fast Fourier Transform (FFT) processing (hereinafter referred to as "FFT processing"). The result of such FFT processing is the frequency spectrum of the beat signal, which is the power value (signal level) for each frequency of the beat signal (for each frequency bin set at frequency intervals corresponding to the frequency resolution).

In the peak extraction process, a peak frequency is extracted as a result of the FFT process by the frequency analysis process. In the peak extraction process, peak frequencies are extracted for each of the "UP section" and the "DN section" of the beat signal, which will be described later.

In the instantaneous data generation process, an angle estimation process is executed to calculate the arrival angle and the power value of the reflected wave corresponding to each of the peak frequencies extracted in the peak extraction process. It should be noted that, at the time of execution of the angle estimation process, the arrival angle is an angle at which it is estimated that the target exists, so it may be referred to as an "estimated angle" below.

Also, in the instantaneous data generation process, a pairing process is executed to determine the correct combination of the peak frequencies of the "UP section" and the "DN section" based on the calculated estimated angle and power value.

Further, in the instantaneous data generation process, the distance of each target from the own vehicle MC and the relative speed toward the own vehicle MC are calculated from the determined combination result. In the instantaneous data processing, the calculated estimated angle, distance and relative velocity of each target are output to the filter processing unit 34 as the instantaneous data 100 for the latest cycle (latest scan), and the history data 36a of the storage unit 36 are output. remember as

3 to 4C show the flow of processing from the pre-processing of the signal processing unit 32 up to this point in the signal processing unit 32 for the sake of easy understanding of the explanation. FIG. 3 is a process explanatory diagram from pre-processing of the signal processing unit 32 to peak extraction processing in the generation unit 33 .

Also, FIG. 4A is a process explanatory diagram of the angle estimation process. 4B and 4C are process explanatory diagrams of the pairing process. Note that FIG. 3 is divided into three regions by two thick downward white arrows. In the following, such regions are described in order as an upper stage, a middle stage, and a lower stage.

As shown in the upper part of FIG. 3, the transmission signal fs(t) is transmitted from the transmission antenna 13 as a transmission wave, is reflected by the target, arrives as a reflected wave, and is received by the reception antenna 21 as the reception signal fr(t ) is received as

At this time, as shown in the upper part of FIG. 3, the received signal fr(t) is delayed by the time difference T with respect to the transmitted signal fs(t) according to the distance between the host vehicle MC and the target. Due to this time difference T and the Doppler effect based on the relative velocities of the host vehicle MC and the target, the beat signal has a frequency fup in an "UP section" where the frequency rises and a frequency fdn in the "DN section" where the frequency drops. is obtained as a repeated signal (see the middle part of FIG. 3).

The lower part of FIG. 3 schematically shows the results of the FFT processing of the beat signal in the frequency analysis processing for each of the "UP section" side and the "DN section" side.

As shown in the lower part of FIG. 3, after the FFT processing, waveforms in the frequency domains on the "UP section" side and the "DN section" side are obtained. In the peak extraction process, the peak frequency of the waveform is extracted.

For example, in the case of the example shown in the lower part of FIG. 3, a peak extraction threshold is used, peaks Pu1 to Pu3 are determined as peaks, and peak frequencies fu1 to fu3 are extracted on the "UP section" side.

Also, on the "DN section" side, the peaks Pd1 to Pd3 are determined as peaks by the same peak extraction threshold, and the peak frequencies fd1 to fd3 are extracted.

Here, the frequency component of each peak frequency extracted by the peak extraction process may be mixed with reflected waves from a plurality of targets. Therefore, in the instantaneous data generation process, an angle estimation process for performing azimuth calculation is performed for each peak frequency, and the presence of a target corresponding to each peak frequency is analyzed.

Note that the azimuth calculation in the instantaneous data generation process can be performed using a known direction-of-arrival estimation technique such as ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques).

FIG. 4A schematically shows an azimuth calculation result of instantaneous data generation processing. In the instantaneous data generation process, the estimated angle of each target (each reflection point) corresponding to each of the peaks Pu1 to Pu3 is calculated from each of the peaks Pu1 to Pu3 of the azimuth calculation result. Also, the magnitude of each of the peaks Pu1 to Pu3 is the power value. In the instantaneous data generation processing, as shown in FIG. 4B, such angle estimation processing is performed for each of the "UP section" side and the "DN section" side.

Then, in the instantaneous data generation process, a pairing process is performed in which peaks having similar estimated angles and power values are combined in the azimuth calculation result. Further, from the result of the combination, in the instantaneous data generation process, the distance of each target (each reflection point) corresponding to the combination of each peak and the relative speed toward the own vehicle MC are calculated.

The distance can be calculated based on the relationship “distance∝(fup+fdn)”. The relative velocity can be calculated based on the relationship "velocity∝(fup-fdn)". As a result, as shown in FIG. 4C, a pairing processing result showing instantaneous data 100 of the estimated angle, distance and relative speed of each reflection point RP with respect to the own vehicle MC is obtained.

Returning to FIG. 2, the filter processing unit 34 will be described. As shown in FIG. 2, the filtering unit 34 includes a prediction unit 34a, an allocation unit 34b, a weighting unit 34c, a resampling unit 34d, and a representative data generation unit 34e.

The filtering unit 34 generates target object data corresponding to the instantaneous data 100 by applying a particle filter that analyzes the instantaneous data 100 generated by the generating unit 33 using a distribution of a predetermined number of particles. .

The particle filter makes multiple hypotheses for the true state of the target and analyzes them. A hypothesis is a hypothetical value for the state of a target, such as position or velocity. For example, in the position space, the hypotheses are scattered in a given distribution and look like one moving particle, so here we refer to the hypotheses as particles. In addition, particle swarm data in which a predetermined number of particles are collectively used as one hypothesis is also used. For example, the particle group data is the average value of the state of particles, and can be said to be one of the most probable hypotheses in the distribution of a predetermined number of particles.

The prediction unit 34a performs particle group data and particle prediction processing. Specifically, the prediction unit 34a assumes that the latest cycle is time t, and predicts the state of the particles and particle group data at time t based on the particles and particle group data at time t−1 of the previous cycle. For example, there is a method of predicting by a motion model and a measurement period ΔT based on the state of particles and particle group data such as velocity and position. That is, in the prediction process, the prediction unit 34a predicts the state of particles and particle group data at time t from the state of past particles and particle group data.

The allocation unit 34b performs processing for allocating the instantaneous data 100 in the latest period to the latest particle group data, which is the prediction result of the prediction unit 34a. Specifically, the allocation unit 34b determines the current is assigned to the particle swarm data.

If there is instantaneous data 100 that does not exist within the allocation range of any target data, the allocation unit 34b treats the instantaneous data 100 as a new target. After performing predetermined processing on the new target, particle group data is generated, and particles are added at the same time. Predetermined processing can also span measurement cycles. For example, for a new target, we simply evaluated the continuity for several cycles and confirmed that it did not occur sporadically due to noise or the like. Particle group data can be generated later and particles can be added.

The weighting unit 34c weights each particle forming the particle group data based on the current instantaneous data 100 assigned to the particle group data by the assignment unit 34b.

For example, the weighting unit 34c increases the weight of particles that are similar to the instantaneous data 100 of this time and decreases the weight of particles that are not similar to the instantaneous data 100 of this time, among the particles of this time. The degree of “similarity” referred to here indicates, for example, an evaluation value of a cost function described based on a positional difference, a speed difference, or the like.

Next, the resampling unit 34d rearranges (resamples) the particles based on the current weight of each particle. Specifically, the resampling unit 34d moves particles with small weights closer to the instantaneous data 100 (larger weights). More specifically, the resampling unit 34d rearranges particles whose weight is less than a predetermined threshold to particle data whose weight is equal to or greater than a predetermined threshold. As a result, the current particle swarm data generated by prediction can be corrected with instantaneous data that may be closer to the true value. As a result, the target data generated by the representative data generation unit 34e, which will be described later, can be corrected with higher accuracy, that is, closer to the true value.

The representative data generation unit 34e generates target data, and generates representative data 50 by aggregating the target data for each identical target. Specifically, first, the representative data generation unit 34e generates target object data based on the current particles rearranged by the resampling unit 34d. For example, the representative data generation unit 34e generates a probability density function from the distribution of particles and generates target data based on the center of gravity thereof, or simply generates target data based on the average of each particle. Note that the particle group data is updated with the representative data.

Subsequently, the representative data generation unit 34e performs grouping by integrating a plurality of target data into one as target data of the same target. For example, when a plurality of target data exists within a predetermined range and the difference between the speed components included in each target data is equal to or less than a predetermined value, the representative data generation unit 34e generates each target data as They are regarded as target data of the same target and grouped.

Thereafter, the representative data generator 34e generates representative data 50 from the grouped target data. Further, the representative data generation unit 34e performs a process of associating the representative data 50 with the type of target.

The representative data generator 34e classifies each target into a preceding vehicle, a stationary object (including a stationary vehicle), and an oncoming vehicle based on the relative speed. For example, the representative data generator 34e classifies a target with a relative speed greater than the speed and direction of the own vehicle MC as a "preceding vehicle".

Also, for example, the representative data generation unit 34e classifies a target with a relative speed generally opposite to the speed of the own vehicle MC as a "stationary object". Also, for example, the representative data generating unit 34e classifies a target having a relative speed that is in the opposite direction to the speed of the own vehicle MC and is greater than the magnitude of this speed as an "oncoming vehicle".

The representative data generation unit 34e acquires travel information including the steering angle and travel speed of the own vehicle MC, calculates the travel speed and travel direction of the own vehicle MC, and obtains the ground speed of the target. Targets may be classified.

Next, the output section 35 will be described. The output unit 35 outputs output data Do obtained by adding target shape data based on the instantaneous data 100 used to generate the representative data 50 to the representative data 50 generated by the filtering unit 34 . The shape data can use arbitrary elements to represent the shape of the target. Which one to use should be determined as appropriate according to system requirements.

For example, when expressing an outline by a sequence of points, the data structure may be such that the positions of a plurality of instantaneous data 100, that is, a set of X and Y coordinates are output. In this case, since the format is similar to one-layer measurement data in, for example, LIDAR, it is possible to use similar contour processing performed at the output destination.

In addition, it is also possible to combine points to express shapes as straight lines, L-shapes, and quadrilaterals, and to output values characteristically representing them as shape data. Regarding the data format of the shape data, image data indicating the shape of the other vehicle LC or coordinate data indicating the shape of the other vehicle LC in a two-dimensional coordinate system based on the radar device 1 may be used. 2 can be appropriately set. Alternatively, the shape data may be compressed and then output, and the compressed shape data may be decompressed at the output destination (for example, the vehicle control device 2) and used.

5A to 5C are diagrams showing specific examples of the output data Do. Note that time t-1 and time t shown below indicate the passage of time, the current time is time t, and the previous scan at time t is time t-1.

As shown in FIG. 5A, for example, when the amount of change in the distance and direction between the representative data 50a at time t−1 and the representative data 50b at time t is equal to or less than a predetermined value, the output data Do at time t includes representative The shape data generated based on the instantaneous data 100a used for the data 50a and the instantaneous data 100b used for generating the representative data 50b are included.

In other words, the output data Do includes shape data based on the past instantaneous data 100 during a period in which the change in the relative distance and direction between the own vehicle MC and the other vehicle LC is equal to or less than a predetermined value.

In other words, during a period in which the relative positional relationship between the own vehicle MC, that is, the radar device 1 and the other vehicle LC, is within a predetermined displacement, the current data is calculated based on the instantaneous data within the period including the past. Generate shape data.

That is, when the relative positional change between the own vehicle MC and the other vehicle LC is small, the current shape data is generated based on the past instantaneous data 100a as well as the current instantaneous data 100b. Note that the instantaneous data 100 may be used as it is for the shape data.

This makes it possible to generate more precise shape data at time t even if the current instantaneous data 100b is small. For example, when the instantaneous data 100b is used for the current shape data, it is possible to increase the number of data constituting the shape data, and it is possible to recognize a more detailed shape. In addition, although the case where the past instantaneous data 100 is used as the current shape data with the own vehicle MC as a reference is shown here, it is not limited to this.

Further, as shown in FIG. 5B, the relative positional relationship between the representative data 50a at the time t−1 and the instantaneous data 100a based on the representative data 50a is stored, and based on this positional relationship, the past It is also possible to generate shape data by correcting the instantaneous data 100a.

That is, even if the positions of the past representative data 50a and the current representative data 50b are different, there is a high possibility that the relative positional relationship between the representative data 50b and the instantaneous data 100a will not change between the past and the current time. Therefore, this time, the shape data is generated based on the relative positional relationship between the instantaneous data 100a and the representative data 50a rather than the past instantaneous data 100a itself. Note that the instantaneous data 100b corrected based on the relative positional relationship may be used as the shape data.

Specifically, as shown in FIG. 5B, the position coordinates of each instantaneous data 100a based on the representative data 50a at time t−1 are stored, and each instantaneous data 100a based on the representative data 50b at time t is stored. to place.

As a result, the output data Do at time t includes instantaneous data 100a at time t-1 and instantaneous data 100b at time t. That is, by storing the coordinate position of each instantaneous data 100 with reference to the representative data 50, even if the relative distance between the own vehicle MC and the other vehicle LC or the angle of the other vehicle LC with respect to the own vehicle MC changes. However, the past instantaneous data 100b can be used to generate the current shape data.

Therefore, even in such a case, more instantaneous data 100 can be used to generate shape data, so that a more detailed shape can be recognized.

Further, in such a case, for example, when the own vehicle MC or the other vehicle LC turns, it is possible to rotate the past instantaneous data 100 according to the turning amount. That is, the above example is a case of parallel movement, and the relative positional relationship between the representative data 50 and the instantaneous data 100 is used as it is. Furthermore, correction by rotation may be performed.

Specifically, as shown in FIG. 5C, for example, when the other vehicle LC turns from time t-1 to time t, the instantaneous data 100 at time t-1 is rotated based on the turning amount of the other vehicle LC. to generate shape data at time t.

In such a case, the speed of the other vehicle LC is determined based on the ground vector of the other vehicle LC obtained by subtracting the vector representing the actual movement component of the own vehicle MC from the vector relating to the relative speed of the own vehicle MC and the other vehicle LC included in the representative data 50b. Detect the amount of turning.

That is, the turning amount of the own vehicle MC is detected based on the steering angle and traveling speed of the own vehicle MC, and the vector based on the movement component of the own vehicle MC is subtracted from the vector based on the relative speed to obtain the turning amount of the other vehicle LC. to detect

This makes it possible to accurately calculate the change in orientation of the own vehicle MC and the other vehicle LC, that is, the amount of turning of the own vehicle MC and the other vehicle LC. Therefore, it is possible to reduce errors in the past instantaneous data 100 based on turning.

In other words, it is possible to improve the likelihood of shape data to be assigned to the output data Do. In the example so far, the case where the instantaneous data 100a at the time t-1 is used as the shape data has been explained, but the instantaneous data 100 before the time t-1 can also be used as the shape data.

Further, the method of detecting the turning amount of the other vehicle LC is not limited to the above example. For example, the radar device 1 may detect the turning amount of the other vehicle LC based on information acquired from another device mounted on the own vehicle MC, or may perform wireless communication such as inter-vehicle communication. The amount of turning may be detected based on the information obtained from the other vehicle LC using the other vehicle LC.

Next, a processing procedure executed by the radar device 1 according to the embodiment will be described with reference to FIG. FIG. 6 is a flow chart showing a processing procedure executed by the radar device 1 according to the embodiment.

As shown in FIG. 6, the generation unit 33 first generates instantaneous data 100 corresponding to the target based on the reflected wave of the transmitted radio wave reflected by the target (step S101).

Subsequently, the prediction unit 34a of the filter processing unit 34 performs prediction processing for predicting the current particle data based on the previous particle data, and prediction data corresponding to the current target data based on the previous target data. Prediction processing for prediction is performed (step S102). Note that the prediction data here means particles and particle group data.

Subsequently, the allocation unit 34b allocates the current instantaneous data 100 to the current particle group data (Step S103). Subsequently, the assigning unit 34b determines the presence or absence of a new target based on the presence or absence of instantaneous data 100 to which the current particle data has not been assigned (step S104). If the instantaneous data 100 is a new target (step S104, Yes), the assigning unit 34b assigns predetermined particle group data (for example, initial state particle group data) to the instantaneous data 100 corresponding to the new target data) is set (step S111).

Subsequently, if the instantaneous data 100 is not a new target (step S104, No), the weighting unit 34c weights the current particle data based on the instantaneous data 100 (step S105).

Subsequently, the resampling unit 34d resamples the current particle group data based on the weighting by the weighting unit 34c (step S106). Subsequently, the representative data generator 34e updates the probability density function of the current resampled particle group data, and generates target data based on the probability density function (step S107).

Subsequently, the representative data generation unit 34e generates the representative data 50 from the target data of the same target (step S108), and the output unit 35 generates the output data Do by giving the shape data to the representative data 50 (step S108). step S109).

Then, the output unit 35 outputs the output data Do (step S110), and ends the process.

As described above, the radar device 1 according to the embodiment includes the generator 33 , the filter processor 34 and the output unit 35 . The generator 33 generates instantaneous data corresponding to the target based on the reflected wave of the transmitted wave reflected by the target. The filter processor 34 generates representative data 50 for each target based on the instantaneous data generated by the generator 33 .

The output unit 35 outputs output data Do obtained by adding target shape data based on the instantaneous data 100 used to generate the representative data 50 to the representative data 50 generated by the filtering unit 34 . Therefore, according to the radar device 1 according to the embodiment, it is possible to correctly recognize the shape of the target.

By the way, in the above-described embodiment, the case where the shape data is generated using the instantaneous data 100 generated by one radar device 1 has been described. It is also possible to generate shape data.

FIG. 7 is a diagram showing a specific example of output data Do according to the modification. Here, a case will be described in which the own vehicle MC is provided with a plurality of radar devices 1A to 1C that detect targets in front of the own vehicle MC from different positions.

For example, radar device 1A shown in FIG. 7 generates shape data based on instantaneous data 100B and instantaneous data 100C generated by radar devices 1B and 1C in addition to instantaneous data 100A generated by radar device 1A. , it is possible to output output data Do including such shape data.

In the example shown in FIG. 7, the radar device 1A generates instantaneous data 100A corresponding to the rear of the other vehicle LC, the radar device 1B generates instantaneous data 100B corresponding to the left side of the other vehicle LC, and the radar device 1C generates A case where instantaneous data 100C corresponding to the right side of the other vehicle LC is generated is shown.

In this case, the radar device 1A selects, for example, the instantaneous data 100 corresponding to the same target from among the instantaneous data 100A-100C, and assigns the selected instantaneous data 100A-100C to the representative data 50 as shape data.

That is, it is possible to give the instantaneous data 100 captured from various angles to the representative data 50 as shape data. By generating the shape data based on the instantaneous data 100 captured from various angles in this way, it is possible to recognize the shape of the target viewed from various angles.

Here, the case where the radar device 1A aggregates the instantaneous data 100 of the other radar devices 1B and 1C has been described, but a separate microcomputer or ECU that aggregates the instantaneous data 100 of the radar devices 1A to 1C may be provided. can be That is, the output unit 35 may be provided in a housing separate from the radar device 1 .

Alternatively, for example, the radar device 1A may be configured to include a plurality of transmission units 10 and a plurality of reception units 20, and the instantaneous data 100 corresponding to the instantaneous data 100B and the instantaneous data 100C may be generated by the radar device 1A. good.

Further, in the above-described embodiment, the case where the shape data is generated based on the instantaneous data 100 has been described, but the present invention is not limited to this. Shape data may be generated using a function. For example, the particles themselves or the probability density function may be used as the shape data as they are, or the shape data may be processed into a predetermined format.

Alternatively, the particle group data may be histogrammed, and the existence probability of the target for each position may be used as the shape data. Even in such a case, it is possible to correctly recognize the shape of the target. A Kalman filter such as an extended Kalman filter (EKF) or an unscented Kalman filter (UKF) may be used to calculate the existence probability.

Further effects and modifications can be easily derived by those skilled in the art. Therefore, the broader aspects of the invention are not limited to the specific details and representative embodiments so shown and described. Accordingly, various changes may be made without departing from the spirit or scope of the general inventive concept defined by the appended claims and equivalents thereof.

1 radar device 33 generation unit 34 filter processing unit 34a prediction unit 34b allocation unit 34c weighting unit 34d resampling unit 34e representative data generation unit 35 output unit 50 representative data 100 instantaneous data Do output data

Claims (8)

a generation unit that generates instantaneous data corresponding to the target based on the reflected wave of the transmitted wave reflected by the target;
a filter processing unit that generates representative data for each target based on the instantaneous data generated by the generation unit;
an output unit for outputting output data obtained by adding shape data of the target based on the instantaneous data used to generate the representative data to the representative data generated by the filtering unit;
and
The output unit
In a period in which the relative positional relationship between the representative data and the radar device is within a predetermined displacement, the shape data is generated based on the instantaneous data in the period,
radar equipment. wherein the instantaneous data within the period includes the past instantaneous data within the period;
The radar device according to claim 1. The output unit
The shape data based on the current instantaneous data and the past instantaneous data corrected based on the current representative data using the positional relationship of the past instantaneous data based on the past representative data. configured to generate a
The radar device according to claim 1 or 2. The output unit
configured to generate the shape data by rotating the past instantaneous data corresponding to the representative data according to the turning amount of the target with respect to the road surface estimated based on the representative data;
The radar device according to claim 3. The output unit
The shape data is calculated based on the instantaneous data based on the transmitted waves transmitted from a plurality of transmitters provided at different positions and the received waves received by the receivers paired with the plurality of transmitters. configured to generate
The radar device according to any one of claims 1 to 4. The filter processing unit is
configured to generate the representative data using a particle filter that analyzes the instantaneous data using a distribution of a predetermined number of particles;
The output unit
configured to generate the shape data based on the distribution shape of the particles;
Radar device according to any one of claims 1 to 5. The radar device is a radar device that is mounted on the own vehicle and detects other vehicles around the own vehicle as the target,
The period is a period in which the amount of change in the relative distance and direction between the own vehicle and the other vehicle is equal to or less than a predetermined value.
Radar device according to any one of claims 1 to 6. a generation step of generating instantaneous data corresponding to the target based on the reflected wave of the transmitted wave reflected by the target;
a filter processing step of generating representative data for each target based on the instantaneous data generated by the generating step;
an output step of outputting output data obtained by adding shape data of the target based on the instantaneous data used to generate the representative data to the representative data generated in the filtering step;
including
In the output step,
generating the shape data based on the instantaneous data during a period in which the relative positional relationship between the representative data and the radar device is within a predetermined displacement;
Target data output method.

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