WO2021077287A1

WO2021077287A1 - Detection method, detection device, and storage medium

Info

Publication number: WO2021077287A1
Application number: PCT/CN2019/112482
Authority: WO
Inventors: 杨晨; 李德建; 刘劲楠; 劳大鹏; 朱金台; 周沐
Original assignee: 华为技术有限公司
Priority date: 2019-10-22
Filing date: 2019-10-22
Publication date: 2021-04-29
Also published as: CN113015922B; CN113015922A

Abstract

A detection method, a detection device, and a storage medium, used in an over-the-horizon radar, a microwave radar, a millimeter wave radar, a laser radar, etc., and used for detecting an object to be tracked. The method comprises: obtaining a spectrum corresponding to a first echo signal that has been received by a radar, and obtaining a range-doppler map according to the first echo signal or a second echo signal that has been received by the radar; and determining one element of a plurality of elements of the range-doppler map in which the difference between an element value and an element value of a noise estimation matrix is greater than or equal to a noise threshold, as said object. During the detection process, the element value comprised by the range-doppler map and the element value comprised by the noise estimation matrix are directly compared with each other; therefore, the missed detection of said object does not happen, which improves the accuracy and efficiency of said object detection.

Description

Detection method, detection device and storage medium

Technical field

This application relates to the field of radar technology, in particular to a detection method, detection device and storage medium.

Background technique

The prior art provides a millimeter wave radar, which has all-weather and all-weather environmental perception capabilities, and can accurately measure the distance and speed of an object to be tracked.

In order to obtain the point cloud data of the object to be tracked in the surrounding environment of the millimeter wave radar, it is necessary to obtain the corresponding range-Doppler map (R-D map) according to the time-frequency signal received by the millimeter wave radar. On this R-D map, the object to be tracked is detected by means of a sliding window constant false alarm ratio (CFAR). For example, the processor can detect the RD map row by row, and the object to be tracked determined by the processor is in the row, and the Doppler velocity of the object to be tracked is greater than the Doppler velocity of the element adjacent to the object to be tracked in the row. The average value of Le speed, for example, the processor can detect the RD map column by column, and the object to be tracked determined by the processor is in the column, and the distance of the object to be tracked is greater than the element adjacent to the object to be tracked in the column The mean value of the distance.

The CFAR method is used to detect the object to be tracked, which requires a large amount of calculation, and it is prone to miss the detection of the object to be tracked in the surrounding environment of the millimeter wave radar, that is, if there are three to be tracked in a row in the RD map. If the Doppler velocity of the object to be tracked in the middle position is greater than the mean value of the Doppler velocities of the two adjacent objects to be tracked, it will cause the damage to the two adjacent objects to be tracked in the middle position. Missed inspection. It can be seen that the efficiency and accuracy of detecting the object to be tracked by CFAR is low.

Summary of the invention

The present application provides a detection method, a detection device, and a storage medium, which can effectively avoid the missed detection of an object to be tracked, thereby effectively improving the efficiency and accuracy of detecting the object to be tracked.

The first aspect of the embodiments of the present invention provides a detection method for detecting an object to be tracked. The method includes: acquiring a spectrum corresponding to a first echo signal received by a radar, and the negative frequency axis of the spectrum is determined by noise. Frequency and corresponding amplitude; determine the noise estimation matrix according to the negative frequency axis of the frequency spectrum, and the noise estimation matrix includes the element of the amplitude or the element of the Doppler velocity converted from the amplitude; An echo signal or the second echo signal received by the radar obtains the range-Doppler matrix; among the multiple elements of the range-Doppler matrix, the element value and the value of an element in the noise estimation matrix An element whose difference is greater than or equal to the noise threshold is determined as the object to be tracked.

In the method shown in this aspect, the element value included in the noise estimation matrix determined according to the negative frequency axis of the spectrum is the amplitude of the noise or the Doppler velocity of the noise. In the process of detecting the object to be tracked in the distance-Doppler matrix, there is no need to compare the size of the element values of multiple elements in the distance-Doppler matrix, but directly through the distance-Doppler matrix. The difference between the Le matrix and the noise estimation matrix, as long as the difference between the element value of the object to be tracked and an element value in the noise estimation matrix is greater than or equal to the noise threshold. Because in the detection process, the element values included in the distance-Doppler matrix are directly compared with the element values included in the noise estimation matrix, there will be no missed detection of the object to be tracked, which improves the detection of the object to be tracked. Accuracy and efficiency.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, the obtaining a spectrum corresponding to the first echo signal that has been received by the radar includes: obtaining a complex signal, the complex signal In order to convert the first echo signal; perform over-sampling on the complex signal to obtain sampled data; perform a first-dimensional fast Fourier transform FFT on the sampled data to obtain the frequency spectrum.

With this implementation manner, by processing the first echo signal, the amplitude corresponding to the noise in the first echo signal can be processed to the negative frequency axis of the spectrum. It can be seen that the noise and non-noise of the first echo signal are effectively distinguished, and the accuracy of detecting the object to be tracked is effectively improved.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, the radar has multiple receiving antennas, and each receiving antenna is used to receive multiple first echo signals, Then, determining the noise estimation matrix according to at least part of the negative frequency of the negative frequency axis of the spectrum includes: acquiring a plurality of initial two-dimensional matrices, the plurality of initial two-dimensional matrices respectively corresponding to the plurality of receiving antennas, the initial two-dimensional matrix including Corresponding to the frequency spectrum of the first echo signal received by the receiving antenna; determine a target two-dimensional matrix, where the target two-dimensional matrix is one of the plurality of initial two-dimensional matrices, or, the target two-dimensional matrix is a pair The multiple initial two-dimensional matrices are superimposed; the noise estimation matrix is determined according to the target two-dimensional matrix.

With this implementation method, the first-dimensional FFT can be performed on the first echo signal to obtain the target two-dimensional matrix. When the second-dimensional FFT has not been performed, the noise estimation matrix can be obtained directly according to the target two-dimensional matrix. , Effectively reducing the number of times to perform FFT, simplifying the calculation process, effectively improving the efficiency of obtaining the noise estimation matrix, and effectively improving the efficiency of detecting the object to be tracked.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, the determining the noise estimation matrix according to the target two-dimensional matrix includes: determining the target distance in the target two-dimensional matrix , The target distance is converted according to the negative frequency included in the frequency spectrum; the target amplitude corresponding to the target distance is determined in the target two-dimensional matrix; the noise estimation matrix is determined, and the noise estimation matrix includes the value The element of the target amplitude.

With this implementation, the value of the elements included in the noise estimation matrix is the magnitude of the noise, which effectively improves the accuracy of detecting the object to be tracked according to the noise estimation matrix.

Based on the first aspect of the embodiments of the present invention, in an optional implementation manner of the first aspect of the embodiments of the present invention, the determining the target amplitude corresponding to the target distance in the target two-dimensional matrix includes: The corresponding amplitudes in the target two-dimensional matrix are averaged to obtain the target amplitude.

If the target range corresponding to the target two-dimensional matrix is F1, F2...FN, the determined target range corresponding to the target distance is F1+F2...FN/N.

Based on the first aspect of the embodiments of the present invention, in an optional implementation manner of the first aspect of the embodiments of the present invention, the determining the target amplitude corresponding to the target distance in the target two-dimensional matrix includes: The corresponding amplitudes in the target two-dimensional matrix are modulus and then averaged to obtain the target amplitude.

If the range of the target distance in the target two-dimensional matrix is F1, F2...FN, the determined target range corresponding to the target distance is:

Based on the modulus value of F1+the modulus value of F2+the modulus value of FN/N in the embodiment of the present invention.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, the determining the target amplitude corresponding to the target distance in the target two-dimensional matrix includes: the target distance is in the Among the multiple amplitudes corresponding to the target two-dimensional matrix, the quantile is determined as the target amplitude. Among them, the quantile can be the median, or quartile, etc.

By adopting the foregoing method of obtaining the target amplitude, the obtained target amplitude can be closer to the actual amplitude of the noise, thereby effectively improving the accuracy of detecting the object to be tracked according to the noise estimation matrix.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, after acquiring a plurality of initial two-dimensional matrices, the method further includes: Perform a second-dimensional FFT to obtain multiple initial distance-Doppler matrices; determine the target distance-Doppler matrix, where the target distance-Doppler matrix is one of the multiple initial distance-Doppler matrices, or , The target range-Doppler matrix is formed by superposing the multiple initial range-Doppler matrices; the noise estimation matrix is determined according to the target range-Doppler matrix.

With this implementation, the first-dimensional FFT can be performed on the first echo signal to obtain a two-dimensional matrix, and then the second-dimensional FFT can be performed on the two-dimensional matrix to obtain the target distance-Doppler matrix, so that The noise estimation matrix obtained according to the target distance-Doppler matrix after the two-dimensional FFT can detect the object to be tracked more accurately, which improves the accuracy of the detection.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, the determining the noise estimation matrix according to the target distance-Doppler matrix includes: Determine the target distance in the Leer matrix, the target distance is converted according to the negative frequency included in the frequency spectrum; determine the target Doppler velocity corresponding to the target distance in the target distance-Doppler matrix; determine the noise An estimation matrix, and the noise estimation matrix includes an element whose value is the Doppler velocity of the target.

With this implementation, the value of the elements included in the noise estimation matrix is the Doppler velocity of the noise, which effectively improves the accuracy of detecting the object to be tracked according to the noise estimation matrix.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, the determining the Doppler velocity corresponding to the target distance in the target distance-Doppler matrix includes: The Doppler velocity corresponding to the target distance in the target distance-Doppler matrix is averaged to obtain the target Doppler velocity.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, the determining the Doppler velocity corresponding to the target distance in the target distance-Doppler matrix includes: Take the modulus value of the Doppler velocity corresponding to the target distance in the target distance-Doppler matrix, and then average to obtain the target Doppler velocity;

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, the determining the Doppler velocity corresponding to the target distance in the target distance-Doppler matrix includes: The target distance is determined as the target Doppler speed among the multiple Doppler velocities corresponding to the target distance-Doppler matrix.

Using the above-mentioned method of obtaining the target Doppler velocity can make the obtained target Doppler velocity closer to the actual Doppler velocity of the noise, thereby effectively improving the accuracy of detecting the object to be tracked according to the noise estimation matrix. Sex.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, before determining the object to be tracked, the method further includes: determining the dimension of the noise estimation matrix and the distance -When the dimensions of the Doppler matrix are not equal, the dimension of the noise estimation matrix is processed, and the dimension of the noise estimation matrix after processing is equal to the dimension of the distance-Doppler matrix.

With this implementation method, when the dimensions of the noise estimation matrix and the distance-Doppler matrix are not equal, the dimension of the noise estimation matrix can be reduced or expanded to make the dimension of the noise estimation matrix If the number is equal to the dimension of the distance-Doppler matrix, the efficiency of the difference between the noise estimation matrix and the distance-Doppler matrix is effectively improved, thereby improving the efficiency of detecting the object to be tracked.

Based on the first aspect of the embodiments of the present invention, in an optional implementation manner of the first aspect of the embodiments of the present invention, before the acquiring the spectrum corresponding to the first echo signal that has been received by the radar, the method further includes: acquiring The signal to be measured of the radar, the signal to be measured is the signal received by the radar from the surrounding environment; the complex signal to be measured is acquired, and the complex signal to be measured is converted from the signal to be measured; the data signal to be measured Perform oversampling to obtain the sampled data to be measured; perform the first-dimensional FFT on the sampled data to be measured to obtain the spectrum to be measured; determine that the amplitude corresponding to any negative frequency included in the negative frequency axis of the spectrum to be measured is less than or equal to the interference Threshold.

In this implementation, it can be detected whether there is an interference signal that interferes with the radar according to the frequency to be measured corresponding to the signal to be measured. If it is determined that the amplitude corresponding to any negative frequency included in the negative frequency axis of the spectrum to be measured is less than or equal to The jamming threshold determines that there is no jamming signal causing interference to the radar currently. Only when there is no interference signal, will the object to be tracked be detected, thereby effectively improving the accuracy of detecting the object to be tracked.

Based on the first aspect of the embodiments of the present invention, in an optional implementation manner of the first aspect of the embodiments of the present invention, the first processing period and the second processing period are preset. In terms of processing timing, the processing timing of the first processing cycle is earlier than the processing timing of the second processing cycle. In the first processing period, the noise estimation matrix is generated based on the first echo signal received by the radar, and in the second processing period, the range-Doppler matrix is generated based on the second echo signal received by the radar. Wherein, the first processing period and the second processing period may have a one-to-one correspondence, that is, the noise estimation matrix obtained in the first processing period is used to compare the noise estimation matrix obtained by the detection device in the second processing period. Time-frequency signal for noise estimation. For another example, the first processing period and the second processing period may have a one-to-many correspondence relationship, that is, the noise estimation matrix obtained in the first processing period is used for the detection device in the subsequent multiple second processing The time-frequency signals obtained by the period are respectively subjected to noise estimation.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, when the noise estimation matrix is a column matrix, and the first element included in the noise estimation matrix When the number of the second elements included in any column of the distance-Doppler matrix is equal, each column element included in the distance-Doppler matrix is different from the noise estimation matrix. Wherein, if the number of the first element included in the noise estimation matrix and the second element included in any column of the distance-Doppler matrix are equal, then the multi-column elements included in the distance-Doppler matrix are column-by-column. Make difference with the noise estimation matrix to obtain the target difference.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, when the noise estimation matrix is a column matrix, and the first element included in the noise estimation matrix If the number of second elements included in any column of the distance-Doppler matrix is equal, the distance-Doppler matrix is a two-dimensional matrix at this time, and the noise estimation matrix is a one-dimensional matrix. The noise estimation matrix is copied so that the matrix dimension of the copied noise estimation matrix is the same as the matrix dimension of the distance-Doppler matrix. Specifically, that the matrix dimension of the copied noise estimation matrix is the same as the matrix dimension of the distance-Doppler matrix means that the number of rows included in the copied noise estimation matrix and the distance-Doppler matrix include The number of rows of is equal, and the number of columns included in the copied noise estimation matrix is equal to the number of columns included in the distance-Doppler matrix.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the first aspect of the embodiments of the present invention, if the number of first elements included in the noise estimation matrix is greater than that of any column of the distance-Doppler matrix The number of included second elements is reduced on the noise estimation matrix, so that the number of elements included in the noise estimation matrix is equal to the number of elements included in any column of the distance-Doppler matrix. If the number of first elements included in the noise estimation matrix is less than the number of elements included in any column of the distance-Doppler matrix, then the noise estimation matrix is expanded so that the first elements included in the noise estimation matrix The number of elements is equal to the number of second elements included in any column of the distance-Doppler matrix.

A second aspect of the embodiments of the present invention provides a detection device for detecting an object to be tracked, including: an acquiring unit, configured to acquire a frequency spectrum corresponding to the first echo signal received by the radar, and the negative frequency axis of the frequency spectrum Composed of the frequency and the corresponding amplitude of the noise; the processing unit is used to determine the noise estimation matrix according to the negative frequency axis of the frequency spectrum, and the noise estimation matrix includes the element taking the value of the amplitude, or the Doppler converted from the amplitude Element of the velocity; obtain the range-Doppler matrix according to the first echo signal or the second echo signal received by the radar; among the multiple elements of the range-Doppler matrix, the element value and the noise In the estimation matrix, an element whose value difference is greater than or equal to the noise threshold is determined as the object to be tracked.

The detection device shown in this aspect executes the detection method shown in the first aspect. For the specific execution process and the description of the beneficial effects, please refer to the above description for details, and details are not repeated.

Based on the second aspect of the embodiment of the present invention, in an optional implementation of the second aspect of the embodiment of the present invention, the acquiring unit is specifically configured to: acquire a complex signal, and the complex signal is to convert the first echo signal The complex signal is over-sampled to obtain sampled data; the first-dimensional fast Fourier transform FFT is performed on the sampled data to obtain the frequency spectrum.

Based on the second aspect of the embodiments of the present invention, in an optional implementation of the second aspect of the embodiments of the present invention, the radar has multiple receiving antennas, and each receiving antenna is used to receive multiple first echo signals, Then, in the process of determining the noise estimation matrix according to at least part of the negative frequency of the negative frequency axis of the frequency spectrum, the processing unit is specifically configured to: obtain a plurality of initial two-dimensional matrices, and the plurality of initial two-dimensional matrices are respectively related to the Corresponding to the antenna, the initial two-dimensional matrix includes the corresponding frequency spectrum of the first echo signal received by the receiving antenna; determining a target two-dimensional matrix, where the target two-dimensional matrix is one of the plurality of initial two-dimensional matrices, Or, the target two-dimensional matrix is formed by superposing the multiple initial two-dimensional matrices; the noise estimation matrix is determined according to the target two-dimensional matrix.

Based on the second aspect of the embodiments of the present invention, in an optional implementation of the second aspect of the embodiments of the present invention, the processing unit is specifically configured to: in the process of determining the noise estimation matrix according to the target two-dimensional matrix: Determine the target distance in the target two-dimensional matrix, the target distance is converted according to the negative frequency included in the frequency spectrum; determine the target amplitude corresponding to the target distance in the target two-dimensional matrix; determine the noise estimation matrix, the The noise estimation matrix includes an element whose value is the target amplitude.

Based on the second aspect of the embodiments of the present invention, in an optional implementation manner of the second aspect of the embodiments of the present invention, the processing unit determines the target amplitude corresponding to the target distance in the target two-dimensional matrix, specifically Used for: averaging the respective amplitudes of the target distance in the target two-dimensional matrix to obtain the target amplitude.

Based on the second aspect of the embodiment of the present invention, in an optional implementation of the second aspect of the embodiment of the present invention, the processing unit is further configured to: perform a second-dimensional FFT on the multiple initial two-dimensional matrices to obtain multiple Initial distance-Doppler matrix; determine the target distance-Doppler matrix, the target distance-Doppler matrix is one of the multiple initial distance-Doppler matrices, or, the target distance-Doppler The matrix is formed by superposing the multiple initial distance-Doppler matrices; the noise estimation matrix is determined according to the target distance-Doppler matrix.

Based on the second aspect of the embodiments of the present invention, in an optional implementation of the second aspect of the embodiments of the present invention, the processing unit is specifically used in the process of determining the noise estimation matrix according to the target distance-Doppler matrix : Determine the target distance in the target distance-Doppler matrix, the target distance is converted according to the negative frequency included in the frequency spectrum; determine the target corresponding to the target distance in the target distance-Doppler matrix Doppler velocity; determine the noise estimation matrix, and the noise estimation matrix includes an element whose value is the target Doppler velocity.

Based on the second aspect of the embodiments of the present invention, in an optional implementation of the second aspect of the embodiments of the present invention, the processing unit determines the Doppler velocity corresponding to the target distance in the target distance-Doppler matrix In the process, it is specifically used for: averaging the Doppler velocity corresponding to the target distance in the target distance-Doppler matrix to obtain the target Doppler velocity.

Based on the second aspect of the embodiments of the present invention, in an optional implementation of the second aspect of the embodiments of the present invention, the processing unit is further configured to: determine the dimension of the noise estimation matrix and the distance-Doppler matrix. When the dimensions are not equal, the dimension of the noise estimation matrix is processed, and the dimension of the noise estimation matrix after processing is equal to the dimension of the distance-Doppler matrix.

Based on the second aspect of the embodiment of the present invention, in an optional implementation of the second aspect of the embodiment of the present invention, the acquiring unit is further configured to: acquire a complex signal to be measured, and the complex signal to be measured is the signal to be measured It is converted into; over-sampling the data signal to be measured to obtain the sampled data to be measured; performing the first-dimensional FFT on the sampled data to be measured to obtain the spectrum to be measured; determine what the negative frequency axis of the spectrum to be measured includes The amplitude corresponding to any negative frequency is less than or equal to the interference threshold.

A third aspect of the embodiments of the present invention provides an electronic device for detecting an object to be tracked, including a transceiver, a memory, and a processor. The memory is used to store a computer program, and the processor is used to read and execute the The computer program in the memory is used to execute the detection method shown in any one of the first aspects of the above-mentioned embodiments of the present invention.

The fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed on a computer, it causes the computer to execute the above-mentioned first embodiment of the present invention. The detection method shown in any one of the aspects.

A fifth aspect of the embodiments of the present invention provides a chip including a processor. The processor is configured to read and execute a computer program stored in the memory to execute any one of the detection methods shown in the first aspect of the foregoing embodiments of the present invention.

Optionally, the chip further includes a memory, and the memory and the processor are connected to the memory through a circuit or a wire.

The sixth aspect of the embodiments of the present invention provides a computer program product. The computer program product includes computer program code. When the computer program code runs on a computer, the computer executes any one of the first aspect of the above-mentioned embodiments of the present invention. The detection method shown.

A seventh aspect of the embodiments of the present invention provides a communication system, including an electronic device and a radar, and the electronic device is configured to execute the detection method shown in any one of the first aspects of the foregoing embodiments of the present invention.

Description of the drawings

Figure 1 is a functional block diagram of a vehicle equipped with millimeter wave radar provided by this application;

Figure 2 is an example diagram of a two-dimensional matrix provided by an existing solution;

FIG. 3 is a flowchart of the steps of an embodiment of the detection method provided by this application;

Figure 4a is a waveform diagram of an embodiment provided by this application;

Figure 4b is a waveform diagram of another embodiment provided by this application;

Fig. 5 is an example diagram of an embodiment of a two-dimensional matrix provided by this application;

Figure 6a is a waveform diagram of an embodiment provided by this application;

Figure 6b is a waveform diagram of another embodiment provided by this application;

FIG. 7 is an example diagram of an embodiment of the noise estimation matrix provided by this application;

FIG. 8 is a flowchart of the steps of an embodiment of the detection method provided by this application;

FIG. 9 is a flowchart of the steps of an embodiment of the detection method provided by this application;

10 is a structural example diagram of an embodiment of the detection device provided by this application;

FIG. 11 is a structural example diagram of an embodiment of an electronic device provided by this application.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the protection scope of the present invention.

The detection method shown in this application is applied to radar. This application does not limit the specific type of radar, for example, over-the-horizon radar, microwave radar, millimeter-wave radar, and lidar. The detection method provided in this application is applied to The millimeter wave radar is taken as an example to illustrate.

The millimeter wave radar is used to scan the surrounding environment by transmitting detection signals to obtain the echo signals reflected by objects in the surrounding environment. The millimeter wave radar processes the received echo signals to obtain time-frequency signals. The processor Based on the time-frequency signal, the object to be tracked in the surrounding environment of the millimeter wave radar is detected, and the millimeter wave radar can track the detected object to be tracked. Among them, the object to be tracked refers to a person or object that is located in the surrounding environment of the millimeter wave radar and reflects the echo signal according to the detection signal. The processor can be integrated in the millimeter-wave radar, and the processor can also be located in a computer device with processing functions connected to the millimeter-wave radar.

Millimeter wave radars are widely used in the fields of national defense, autonomous driving, and geographic surveying and mapping. The following is an example of the application of millimeter wave radars provided by this application in the field of autonomous driving as shown in Figure 1. It should be clear that: The description of the field to which the millimeter wave radar is applied in this embodiment is an optional example, and is not specifically limited.

As shown in Fig. 1, Fig. 1 is a functional block diagram of a vehicle 100 with an automatic driving function provided by an embodiment of the present application. In one embodiment, the vehicle 100 is configured in a fully or partially autonomous driving mode. For example, the vehicle 100 can control the vehicle 100 itself while in the automatic driving mode, and can determine the current state of the vehicle and its surrounding environment through human operations, determine the possible behavior of at least one other vehicle in the surrounding environment, and determine the The confidence level corresponding to the possibility of other vehicles performing possible behaviors is controlled based on the determined information. When the vehicle 100 is in the automatic driving mode, the vehicle 100 can be placed to operate without human interaction.

The vehicle 100 may include various subsystems, such as a travel system 102, a sensor system 104, a control system 106, one or more peripheral devices 108 and a power supply 110, a computer system 122, and a user interface 116. Alternatively, the vehicle 100 may include more or fewer subsystems, and each subsystem may include multiple elements. In addition, each subsystem and element of the vehicle 100 may be interconnected by wire or wirelessly.

The travel system 102 may include components that provide power movement for the vehicle 100. In one embodiment, the travel system 102 may include an engine 118, an energy source 119, a transmission 120, and wheels/tires 121.

The sensor system 104 may include several sensors that sense information about the environment around the vehicle 100. For example, the sensor system 104 may include a positioning system 129 (the positioning system may be a global positioning system (GPS) system, a Beidou system or other positioning systems), an inertial measurement unit (IMU) 124, Radar 126, laser rangefinder 128, and camera 130. Sensor data from one or more of these sensors can be used to detect objects and their corresponding characteristics (position, shape, direction, speed, etc.). Such detection and identification are key functions for the safe operation of the autonomous vehicle 100.

The radar 126 may use detection signals to sense objects to be tracked in the surrounding environment of the vehicle 100. In this embodiment, the radar 126 is a millimeter-wave radar. The millimeter-wave radar has all-weather and all-weather environmental perception capabilities. The detection range of the millimeter-wave radar is generally between 150 meters and 250 meters, and some have high performance. The detection range of millimeter wave radar can even reach 300 meters, which can meet the needs of detecting a larger range when the vehicle is moving at high speed. At the same time, the detection accuracy of millimeter-wave radar is high, and it can accurately measure the distance and speed of the object to be tracked, thereby providing differentiated competitiveness that other vehicle-mounted sensors do not have. This embodiment does not limit the specific type of the millimeter wave radar. In this example, the millimeter wave radar is frequency modulated continuous wave (FMCW) as an example for illustration.

The laser rangefinder 128 can use laser light to sense the object to be tracked in the environment where the vehicle 100 is located. In some embodiments, the laser rangefinder 128 may include one or more laser sources, laser scanners, and one or more detectors, as well as other system components.

The control system 106 controls the operation of the vehicle 100 and its components. The control system 106 may include various components, including a steering system 132, a throttle 134, a braking unit 136, a sensor fusion algorithm 138, a computer vision system 140, a route control system 142, and an obstacle avoidance system 144.

Of course, in one example, the control system 106 may add or alternatively include components other than those shown and described. Alternatively, a part of the components shown above may be reduced.

The vehicle 100 interacts with external sensors, other vehicles, other computer systems, or users through peripheral devices 108. The peripheral device 108 may include a wireless communication system 146, an onboard computer 148, a microphone 150, and/or a speaker 152.

In some embodiments, the peripheral device 108 provides a means for the vehicle 100 to interact with the user interface 116. The power supply 110 may provide power to various components of the vehicle 100. Part or all of the functions of the vehicle 100 are controlled by the computer system 122. The computer system 122 may include at least one processor 113 that executes instructions 115 stored in a non-transitory computer readable medium such as the memory 114. The computer system 122 may also be multiple computing devices that control individual components or subsystems of the vehicle 100 in a distributed manner.

The processor 113 may be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor may be a dedicated device such as an application specific integrated circuit (ASIC) or other hardware-based processor. Although FIG. 1 functionally illustrates the processor, memory, and other elements of the computer system 122 in the same block, those of ordinary skill in the art should understand that the processor, computer, or memory may actually include Multiple processors, computers, or memories that are not stored in the same physical enclosure. For example, the memory may be a hard disk drive or other storage medium located in a housing other than the computer system 122. Therefore, a reference to a processor or computer will be understood to include a reference to a collection of processors or computers or memories that may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components such as the steering component and the deceleration component may each have its own processor that only performs calculations related to component-specific functions.

In the various aspects described herein, the processor may be located away from the vehicle and wirelessly communicate with the vehicle. In other aspects, some of the processes described herein are executed on a processor arranged in the vehicle and others are executed by a remote processor, including taking the necessary steps to perform a single manipulation.

In some embodiments, the memory 114 may include instructions 115 (for example, program logic), and the instructions 115 may be executed by the processor 113 to perform various functions of the vehicle 100, such as performing the functions of the detection method shown in the present application. The memory 114 may also contain additional instructions, including those for sending data to, receiving data from, interacting with, and/or controlling one or more of the traveling system 102, the sensor system 104, the control system 106, and the peripheral device 108. instruction.

In addition to the instructions 115, the memory 114 may also store data, such as road maps, route information, the location, direction, and speed of the vehicle, and other such vehicle data, as well as other information. Such information may be used by the vehicle 100 and the computer system 122 during the operation of the vehicle 100 in autonomous, semi-autonomous, and/or manual modes.

The user interface 116 is used to provide information to or receive information from a user of the vehicle 100. Optionally, the user interface 116 may include one or more input/output devices in the set of peripheral devices 108, such as a wireless communication system 146, an in-vehicle computer 148, a microphone 150, and a speaker 152.

The computer system 122 may control the functions of the vehicle 100 based on inputs received from various subsystems (for example, the travel system 102, the sensor system 104, and the control system 106) and from the user interface 116. For example, the computer system 122 may utilize input from the control system 106 in order to control the steering unit 132 to avoid obstacles detected by the sensor system 104 and the obstacle avoidance system 144. In some embodiments, the computer system 122 is operable to provide control of many aspects of the vehicle 100 and its subsystems.

Optionally, one or more of these components described above may be installed or associated with the vehicle 100 separately. For example, the storage 114 may exist partially or completely separately from the vehicle 100. The aforementioned components may be communicatively coupled together in a wired and/or wireless manner.

Optionally, the above-mentioned components are only an example. In actual applications, the components in the above-mentioned modules may be added or deleted according to actual needs. FIG. 1 should not be construed as a limitation to the embodiments of the present application.

An autonomous vehicle traveling on a road, such as the vehicle 100 above, can identify objects to be tracked in its surrounding environment to determine the adjustment to the current speed of the vehicle. The object to be tracked may be other vehicles, traffic control equipment, or pedestrians. In some examples, each identified object to be tracked can be considered independently, and based on the respective characteristics of the object to be tracked, such as its current speed, acceleration, distance from the vehicle, etc., can be used to determine the adjustments required by the self-driving car speed.

Optionally, the self-driving car vehicle 100 or the computing device associated with the self-driving vehicle 100 (such as the computer system 122, the computer vision system 140, and the memory 114 in FIG. 1) may be based on the characteristics of the identified object to be tracked and the surrounding environment To predict the behavior of the identified object to be tracked (for example, traffic, rain, ice on the road, etc.). Optionally, each identified object to be tracked depends on each other's behavior, so all identified objects to be tracked can also be considered together to predict the behavior of a single identified object to be tracked. The vehicle 100 can adjust its speed based on the predicted behavior of the identified object to be tracked. In other words, the self-driving car can determine what stable state the vehicle will need to adjust to (for example, accelerate, decelerate, or stop) based on the predicted behavior of the object to be tracked. In this process, other factors may also be considered to determine the speed of the vehicle 100, such as the lateral position of the vehicle 100 on the road, the curvature of the road, the proximity of static and dynamic objects to be tracked, and so on.

In addition to providing instructions to adjust the speed of the self-driving car, the computing device can also provide instructions to modify the steering angle of the vehicle 100 so that the self-driving car follows a given trajectory and/or maintains an object to be tracked near the self-driving car (For example, a car in an adjacent lane on a road) The safe horizontal and vertical distance.

The above-mentioned vehicle 100 may be a car, truck, motorcycle, bus, boat, airplane, helicopter, lawn mower, recreational vehicle, playground vehicle, construction equipment, tram, golf cart, train, and trolley, etc. The application examples are not particularly limited.

The following describes the defects of millimeter wave radar detecting objects to be tracked in the surrounding environment:

In order to realize the detection of the object to be tracked, a millimeter-wave radar is required to transmit a detection signal to the surrounding environment and receive the echo signal reflected by the object to be tracked in the surrounding environment. After the millimeter wave radar receives the echo signal, it performs front-end processing on the echo signal to obtain the time-frequency signal. The front-end processing includes filtering, amplification, analog-to-digital conversion (AD), and so on. The processor can obtain the RD map according to the time-frequency signal. The processor detects the object to be tracked in the RD map. The processor can track the object to be tracked based on the detected object to be tracked, for example, if the object to be tracked For pedestrians or other vehicles in the surrounding environment, the processor can track people or vehicles in automatic driving.

In order to better understand the detection process of the object to be tracked, the following is an example description of the R-D map:

Millimeter-wave radars usually use multiple antennas to achieve multiple transmissions and multiple receptions, thereby improving the resolution of millimeter-wave radars. Specifically, millimeter-wave radars are deployed with transmitting antennas for transmitting detection signals to the surrounding environment and return signals for receiving reflections from the surrounding environment. Receiving antenna for wave signals. More specifically, any transmitting antenna deployed by the millimeter-wave radar transmits detection signals to the surrounding environment in units of frames, and the millimeter-wave radar receives multiple echo signals through the receiving antenna.

The processor can obtain the multiple time-frequency signals output by the millimeter-wave radar after performing front-end processing on multiple echo signals, and the multiple time-frequency signals are uniform and equally spaced. The processor samples each of the multiple time-frequency signals according to a preset sampling point. This example does not limit the specific size of the sampling rate for sampling each time-frequency signal. For example, if the processor determines that the number of preset sampling points is 125, the processor samples the time-frequency signal and obtains 125 sampling points to perform the first fast Fourier transform (FFT). To obtain the distance corresponding to the signal frequency collected at each sampling point. Wherein, the distance corresponding to each sampling point refers to the physical distance between the object used to reflect the signal corresponding to each sampling point and the millimeter wave radar. The processor stores the distance corresponding to each sampling point in a two-dimensional matrix in the form of a column, and the column includes 125 elements sorted from small to large distance. It should be clarified that here, the processor stores the distance corresponding to each sampling point in a two-dimensional matrix in the form of a column as an example for illustration. In other examples, the processor can also set the distance corresponding to each sampling point. The distance is stored in a two-dimensional matrix in the form of rows.

For a better understanding, please refer to Figure 2, where Figure 2 is an example of a two-dimensional matrix. In the two-dimensional matrix shown in Figure 2, the processor obtains 8 time-frequency signals, and After each time-frequency signal is sampled, the first-dimensional FFT is performed, so that the two-dimensional matrix shown in FIG. 2 includes 8 columns of objects, and the distance of each element included in each column increases in sequence in the direction shown by the arrow 220. The unit of the distance in the example may be meters (m).

After the processor sets all the received time-frequency signals in the two-dimensional matrix shown in Figure 2, the processor can perform a second-dimensional FFT on all elements included in each row of the two-dimensional matrix to obtain The Doppler velocity of each element in the row. The unit of the Doppler velocity can be meters per second (m/s). Among them, the Doppler velocity of each element included in each row increases in the direction shown by the arrow 221.

After the processor completes the two-dimensional FFT shown above, the processor can determine that the two-dimensional matrix shown in Figure 2 is the RD map. The following is based on the RD map shown in Figure 2 for the processor, how to use the RD map The specific process of detecting the object to be tracked is illustrated as an example:

As shown in Figure 2, the processor is based on cell average-constant false alarm ratio (CA-CFAR) or order statistic-constant false alarm ratio (OS-CFAR) ) And other methods, through the detection method of sliding window CFAR, the detection of the object to be tracked in the RD map is realized. The specific process is as follows:

The processor first performs Doppler sliding window detection line by line according to the direction of the RD map row. Take the fifth line of the RD map, which is shown in line 201, as an example, perform Doppler sliding window detection on the line 201 of the object to be tracked. The condition that 202 satisfies is that the Doppler velocity of the object 202 to be tracked is greater than the average value of the Doppler velocities of the element 203 and the element 204 adjacent to the object 202 to be tracked in the row 201.

After the processor detects the object to be tracked on each row of the RD map, the processor performs distance sliding window detection on all the columns included in the RD map column by column. Taking column 205 of the RD map as an example, the distance is performed on the column 205 The condition that the object to be tracked 206 detected by the sliding window satisfies is that the distance of the object to be tracked 206 is greater than the average value of the distances of the

elements

207 and 208 adjacent to the object to be tracked 206 in the column 205.

The processor can take the intersection of the tracked object detected row by row and the tracked object detected row by column, that is, the processor determines that the object to be tracked is detected by the Doppler sliding window and the distance sliding window for tracking. The object to be tracked detected by the Doppler sliding window row by row and the object to be tracked detected by the distance sliding window row by column can also be combined, that is, the processor determines that the object to be tracked is detected by any dimension sliding window. The tracked object to be tracked is tracked. The sliding window CFAR detection mentioned here has many specific implementation forms, such as. Different implementation forms have different performance and computational complexity. After the processor detects the object to be tracked, based on the R-D map, the distance and Doppler velocity corresponding to the object to be tracked can be determined, so as to realize the tracking of the object to be tracked.

The following describes the detection method based on sliding window CFAR to detect the defects of the object to be tracked on the R-D map:

Using this sliding window CFAR detection method requires a relatively large amount of calculation, especially as mentioned above, using the OS-CFAR implementation form, the amount of calculation will be greater, thereby reducing the efficiency of detecting the object to be tracked. Moreover, no matter which implementation form of CFAR, there are certain inherent defects. For example, a sliding window CFAR detection method is very easy to cause missed detection of the object to be tracked, thereby reducing the accuracy of detecting the object to be tracked.

As shown in Figure 2, the detection method based on sliding window CFAR is very easy to cause the situation that the object to be tracked is concealed by the object to be tracked strongly. Specifically, the situation in which the object to be tracked is strongly concealed by the object to be tracked is that in the RD map, the object to be tracked with a larger distance and/or Doppler velocity will cover the object with a smaller distance and/or Doppler. The speed of the object to be tracked will make it impossible for the processor to detect the object to be tracked with a small distance and/or Doppler velocity, resulting in the occurrence of missed detection of the object to be tracked. As shown in FIG. 2, if the Doppler velocity of the object to be tracked 211 is large, the Doppler velocity of the object to be tracked 209 is small, and the Doppler velocity of the object to be tracked 209 is smaller than the adjacent object to be tracked The average value of the Doppler velocities of 211 and element 210 is due to the existence of the object to be tracked 211 with a large Doppler velocity, so that the object to be tracked 211 with a large Doppler velocity conceals the object to be tracked with a small Doppler velocity. 209. Furthermore, the processor cannot successfully detect the object 209 to be tracked. It can be seen that the detection method based on the sliding window CFAR reduces the accuracy of detecting the object to be tracked. It can be seen that the detection method based on the sliding window CFAR can easily cause the missed detection of the object to be tracked when multiple objects to be tracked are adjacent.

In order to improve the efficiency and accuracy of detecting the object to be tracked, this application provides a detection method. The specific implementation process of the detection method provided by this application will be exemplified below in conjunction with FIG. 3, where FIG. 3 is A step flow chart of an embodiment of the detection method provided by the application.

Step 301: The radar obtains the first echo signal.

In order to improve the accuracy and efficiency of tracking the object to be tracked in the radar shown in this embodiment, the radar is configured with multiple receiving antennas, and each receiving antenna is used to receive multiple first echo signals. For example, the radar is configured with N receiving antennas, and N is a positive integer greater than 1. Then the radar can receive multiple first echo signals based on any receiving antenna.

Step 302: The radar sends the time-frequency signal to the detection device.

The detection device shown in this embodiment is used to track the object to be tracked. This embodiment does not limit the specific type of the detection device. The detection device can be integrated in the radar or set separately from the radar. For example, the detection device and radar are separately set as an example.

The radar can generate multiple time-frequency signals after the multiple first echo signals that have been acquired through front-end processing respectively, and send the multiple time-frequency signals to the detection device. For the specific process of front-end processing, please refer to the above description, and the details are not repeated.

Step 303: The detection device converts the time-frequency signal into a complex signal.

Specifically, the detection device converts multiple first echo signals received by each receiving antenna of the radar into complex signals, respectively. It should be clarified that, in this embodiment, the detection device obtains the corresponding complex signal according to the time-frequency signal as an example for illustrative description. In other examples, the radar can also generate the corresponding signal according to the received first echo signal. The complex signal is then sent to the detection device by the radar.

The following describes the process of how the detection device obtains the corresponding complex signal according to the time-frequency signal:

After the detection device obtains the time-frequency signal, the detection device can divide the time-frequency signal into two signals, namely the first signal and the second signal. The detection device shifts the first signal by 90 degrees, and the detection device determines a complex signal according to the first signal and the second signal, wherein the real part of the complex signal is the second signal, and the imaginary part is 90 degrees The first signal moved backward.

Step 304: The detection device performs over-sampling on each complex signal to obtain sampled data.

In this embodiment, when the detection device converts the M first echo signals received by the receiving antenna i into M complex signals, the detection device can target the M complex signals at the first sampling rate. The signal is sampled at the first sampling rate to obtain M sampling data. It can be seen that the M sampling data is formed by over-sampling the detection device according to the M complex signals. Wherein, the receiving antenna i is one of the N receiving antennas of the radar, and the first sampling rate shown in this embodiment is oversampling, that is, as long as the first sampling rate is any value greater than 1 For example, the first sampling rate can be 1.5 or 2. The sampling data acquired by the detection device through the first sampling rate includes the sampling time corresponding to each sampling point and the frequency corresponding to each sampling point.

Step 305: The detection device performs first-dimensional FFT processing on the sampled data to obtain a frequency spectrum.

For a better understanding, the following takes the first sampling rate of 2 as an example for illustration:

First, referring to Figure 4a, Figure 4a shows the sampled data obtained by the detection device after sampling the complex signal at the first sampling rate, where the sampled data can be the waveform shown in Figure 4a. The unit of the abscissa of the waveform graph is the time of sampling, the unit is microsecond (us), etc., which are not specifically limited, and the ordinate of the waveform graph is the amplitude.

The detection device performs the first-dimensional FFT processing on the waveform diagram shown in FIG. 4a to obtain the frequency spectrum shown in FIG. 4b, where the frequency component of the complex signal is taken as the abscissa and the amplitude is taken as the ordinate.

In this embodiment, the first sampling rate is 2 as an example for description. The detection device samples the complex signal with a first sampling rate of 2 to obtain the frequency spectrum. The frequency spectrum generated by the detection device is divided into a positive frequency axis and a negative frequency axis. The amplitude corresponding to the signal reflected by the effective target appears on the positive frequency axis of the spectrum, and the amplitude corresponding to the noise appears on the negative frequency axis of the spectrum.

It can be seen that, as shown in this step, the detection device can obtain the corresponding M frequency spectra respectively for the M first echo signals received by the receiving antenna i.

Step 306: The detection device obtains a two-dimensional matrix corresponding to each receiving antenna.

For example, the detection device can obtain M frequency spectra corresponding to the M first echo signals received by the receiving antenna i, and the detection device can set the M frequency spectra corresponding to the same receiving antenna i in a two-dimensional matrix. . The two-dimensional matrix is the two-dimensional matrix corresponding to the receiving antenna i. The specific process of obtaining the two-dimensional matrix is described below:

The detection device converts the target frequency in the target frequency spectrum into a distance. The target frequency spectrum is any frequency spectrum of the M frequency spectrums corresponding to the receiving antenna i, and the target frequency is one of multiple frequencies included in the target frequency spectrum. The detection device sets the corresponding relationship between the distance and the amplitude in a two-dimensional matrix. Wherein, the distance and amplitude both correspond to the target frequency.

Specifically, the detection device can convert the target frequency of the spectrum into a distance in the following manner:

R=c*f/2/S. Among them, c is the speed of light, S is the sweep spectrum of the radar, R is the target distance, and f is the frequency at which the radar emits the detection signal.

The detection device can set the M first echo signals received by the receiving antenna i in the two-dimensional matrix corresponding to the receiving antenna i in the above-mentioned manner. It can be seen that the detection device can respectively create a two-dimensional matrix for the first echo signals received by different receiving antennas. For example, if a radar has a receiving antenna 1, a receiving antenna 2,... a receiving antenna N, the detection device is directed to the receiving antenna 1 Generate a two-dimensional matrix 1 for all the first echo signals received by the receiving antenna 2. Generate a two-dimensional matrix for all the first echo signals received by the receiving antenna 2 ... For all the first echo signals received by the receiving antenna N The wave signal generates a two-dimensional matrix N.

The following is an exemplary description of the two-dimensional matrix corresponding to the receiving antenna i in conjunction with FIG. 5:

If the receiving antenna i of the radar receives 6 first echo signals (that is, the first echo signal 1, the first echo signal 2... the first echo signal 6), the detection device measures each first echo signal The converted data is sampled at 8 sampling points (ie, sampling point 1, sampling point 2...sampling point 8). The detection device can obtain the two-dimensional matrix 501 as shown in FIG. 5 for the receiving antenna i.

Taking the first behavior example included in the two-dimensional matrix, the detection device samples the sampling data corresponding to the first echo signal according to the sampling point 1 to obtain the corresponding frequency, and the detection device converts the frequency into a distance D1. . The detection device determines the amplitude F1 corresponding to the distance D1 according to the first echo signal 1, determines the amplitude F2 corresponding to the distance D1 according to the first echo signal 2 ... and determines the corresponding amplitude F6 according to the first echo signal 6. The detection device can form the first row of the two-dimensional matrix according to the above-mentioned parameters, that is, the first row includes the respective amplitudes of the distance D1 in the six first echo signals. By analogy, the detection device can obtain a two-dimensional matrix 501 as shown in FIG. 5.

Step 307: The detection device determines the target two-dimensional matrix.

In this embodiment, when the detection device acquires multiple two-dimensional matrices respectively corresponding to all the receiving antennas of the radar, the detection device may determine the target two-dimensional matrix according to the multiple two-dimensional matrices. Several optional methods for the detection device to obtain the target two-dimensional matrix are described below:

Way 1

The detection device can determine that the target two-dimensional matrix is one of a plurality of two-dimensional matrices.

Way 2

Taking a radar with N receiving antennas as an example, the detection device can obtain N two-dimensional matrices corresponding to the N receiving antennas. The detection device can superimpose N two-dimensional matrices to obtain the target two-dimensional matrix.

This embodiment does not limit the process of how the detection device superimposes N two-dimensional matrices. For example, the detection device can average the element values of all elements in the same dimension in the N two-dimensional matrices to obtain the superimposed The element value, the element value after the superposition is an element value included in the target two-dimensional matrix. For another example, the detection device may take the modulus of the element values of all elements in the same dimension in the N two-dimensional matrices and then average them to obtain the superimposed element value. For another example, the detection device determines the quantile as the element value after superposition among the element values of all elements in the same dimension in N two-dimensional matrices.

Step 308: The detection device determines at least one target distance in the target two-dimensional matrix.

In this embodiment, the detection device can estimate noise according to the determined target distance. The process of determining the target distance by the detection device will be described below:

The detection device determines the target distance in the target two-dimensional matrix. The target distance is converted according to the negative frequency included in the frequency spectrum. For the description of the process of converting the negative frequency into the distance, please refer to the above-mentioned frequency-based The process of converting to distance will not be described in detail.

As shown in FIG. 4b, it can be seen that the frequency spectrum shown in FIG. 4b includes a positive frequency axis and a negative frequency axis, wherein the positive frequency axis includes a positive frequency with a positive value, and the negative frequency axis includes a frequency with a negative value. Due to the over-sampling of the complex signal, the frequency corresponding to the object to be tracked to be detected appears on the positive frequency axis, while the negative frequency axis of the spectrum only has noise. Therefore, in this embodiment, noise can be estimated based on the negative frequency included in the negative frequency axis of the spectrum.

The target distance shown in this embodiment can be converted from any negative frequency on the negative frequency axis. This embodiment does not limit the number of determined target distances, as long as it is one or more.

Secondly, the detection device can determine the target distance respectively corresponding to the at least one first negative frequency in the target two-dimensional matrix.

Step 309: The detection device determines a noise estimation matrix.

In this embodiment, the detection device determines the target amplitude according to the target distance in the two-dimensional matrix of the determined target. The detection device can determine that an element value of a first element included in the noise estimation matrix is the target amplitude. In the case where the detection device determines multiple target amplitudes, the element values of the multiple first elements included in the noise estimation matrix are respectively multiple target amplitudes. The following describes the process of the detection device determining the target amplitude according to the target distance:

First, the detection device determines the amplitude corresponding to the target distance in a two-dimensional matrix.

Continuing to refer to FIG. 5, if the detection device determines that the target distances are D1, D2, D3, and D4, the detection device can determine in the target two-dimensional matrix that the amplitudes corresponding to D1 are F1, F2...F6; The amplitudes corresponding to D2 are F1, F2...F6, and so on, and the amplitudes corresponding to D4 are determined to be F1, F2...F6.

Secondly, the detection device determines the target amplitude according to the amplitudes respectively corresponding to the target distances, and several optional methods of determination are exemplified as follows:

Way 1

The detection device averages the respective amplitudes of the target distance in the target two-dimensional matrix to obtain the target amplitude. This embodiment does not limit the specific averaging algorithm. For example, it may be arithmetic average, geometric average, square average, harmonic average, or weighted average.

Continuing to take the above example as an example, the target amplitude corresponding to the target distance D1 determined by the detection device is F1+F2+F3+F4+F5+F6/6.

Way 2

The detection device takes the modulus value of the amplitude corresponding to the target distance in the target two-dimensional matrix, and then averages the target amplitude to obtain the target amplitude.

Continuing to take the above example as an example, the modulus of the target amplitude F1 corresponding to the target distance D1 determined by the detection device + the modulus of F2 + the modulus of F3 + the modulus of F4 + the modulus of F5 + the modulus of F6/ 6.

Way 2

The detection device determines the quantile as the target amplitude among the multiple amplitudes corresponding to the target two-dimensional matrix. Wherein, the quantile may be the median, or quartile, etc., which is not specifically limited in this embodiment.

Continuing to take the above example as an example, if the detection device determines that the quantile is the median, the detection device can determine that the target amplitude corresponding to the target distance D1 is F4.

By analogy, the detection device can obtain the target amplitude corresponding to the target distance D2, the target amplitude corresponding to the target distance D3, and the target amplitude corresponding to the target distance D4. That is, the target two-dimensional matrix 501 shown in FIG. 5 is converted into the noise estimation matrix 502 shown in FIG. 5. It can be seen that in the example shown in FIG. 5, the noise estimation matrix is a one-dimensional column matrix, and the element value of the first first element included in the noise estimation matrix is the target amplitude corresponding to the target distance D1, and the second second The element value of the element is the target amplitude corresponding to the target distance D2 and so on.

It should be clarified that, in this embodiment, the noise estimation matrix is a column matrix as an example for exemplification, and it is not limited. In other examples, the noise estimation matrix may also be a row matrix.

Optionally, in order to improve the accuracy of noise estimation, the detection device may multiply the preset gain to the target amplitude. This embodiment does not limit the magnitude of the preset gain, as long as the result obtained after the target amplitude is multiplied by the preset gain can be more consistent with the amplitude of the noise.

Step 310: The detection device obtains the range-Doppler matrix.

The detection device generates a range-Doppler matrix according to multiple echo signals received by the radar, and realizes the detection of the object to be tracked through the range-Doppler matrix.

Optionally, the detection device shown in this embodiment may obtain the range-Doppler matrix according to the first echo signal shown above. That is, the detection device completes the acquisition of the noise estimation matrix according to the multiple first echo signals that the radar has received, and also completes the acquisition of the range-Doppler matrix.

Optionally, the second echo signal may be an echo signal different from the first echo signal. That is, the detection device obtains the noise estimation matrix for the first echo signal, and obtains the detection range-Doppler matrix to be detected for the second echo signal. For example, the detection device can preset the first processing period and the second processing period. This embodiment does not limit the specific duration of the first processing period and the second processing period, as long as the processing timing of the first processing period is earlier than the processing timing of the second processing period in terms of processing timing. The detection device may generate the noise estimation matrix based on the first echo signal received by the radar in the first processing period, and the detection device may generate the noise estimation matrix based on the second echo signal received by the radar in the second processing period Generate distance-Doppler matrix. For the process of generating the range-Doppler matrix, please refer to the description of generating the range-Doppler matrix according to the echo signal shown above, and the details are not repeated here. In this embodiment, the detection device acquires the noise estimation matrix based on the first echo signal, and the detection device acquires the range-Doppler matrix based on the second echo signal as an example for illustration.

If the second echo signal can be an echo signal different from the first echo signal, the detection device can sample the complex signal of the first echo signal at the first sampling rate to obtain noise Estimating the matrix, the detection device can sample the complex signal of the second echo signal at the second sampling rate to obtain the range-Doppler matrix. For a specific description of the first sampling rate, please refer to the above description. This embodiment does not limit the specific size of the second sampling rate, as long as the second sampling rate is also oversampling.

Optionally, this embodiment is described with an example in which the second sampling rate is less than the first sampling rate. Because the second sampling rate is lower, the detection device can obtain greater ranging performance. In this embodiment, the first sampling rate is set to be 2 and the second sampling rate is set to be 1 as an example for illustration. The following describes how the detection device obtains the range-Doppler matrix:

First, the detection device converts each second echo signal received by the receiving antenna i of the radar into a complex signal;

Secondly, the detection device samples each complex signal at a second sampling rate to obtain sampled data. In this embodiment, the second sampling rate is 1. For a better understanding, the following is shown in conjunction with Figure 6a, where Figure 6a Shown is the sampling data obtained by the detection device after sampling the complex signal at the second sampling rate, where the sampling data can be a waveform diagram as shown in FIG. 6a.

Third, the detection device performs the first-dimensional FFT processing on the waveform diagram shown in FIG. 6a to obtain the frequency spectrum shown in FIG. 6b;

Third, the detection device obtains a two-dimensional matrix corresponding to the receiving antenna i, and performs a second-dimensional FFT on the two-dimensional matrix to obtain a range-Doppler matrix;

Finally, the detection device determines the range-Doppler matrix. Wherein, the distance-Doppler matrix may be one of the distance-Doppler matrices corresponding to the multiple receiving antennas, or the distance-Doppler matrix may be the distance-doppler matrix corresponding to the multiple receiving antennas. Puller matrix is superimposed.

Step 311: The detection device makes a difference between the range-Doppler matrix and the noise estimation matrix to obtain a target difference.

The detection device shown in this embodiment can make the difference between the range-Doppler matrix and the noise estimation matrix as shown in the following way, and then obtain the target difference of the target second element included in the range-Doppler matrix The second element of the target is any second element included in the distance-Doppler matrix. Hereinafter, several optional implementation manners for acquiring the target difference value by the detection device will be exemplified:

Way 1

In the case that the noise estimation matrix is a column matrix, and the detection device determines that the number of the first element included in the noise estimation matrix and the second element included in any column of the distance-Doppler matrix are equal Next, the detection device can make a difference between each column element included in the distance-Doppler matrix and the noise estimation matrix.

Specifically, if the number of the first element included in the noise estimation matrix and the second element included in any column of the distance-Doppler matrix are equal, the detection device may include the distance-Doppler matrix. Multi-column elements are compared with the noise estimation matrix column by column to obtain the target difference.

Optionally, if any column of the distance-Doppler matrix includes a plurality of second elements arranged in descending order of distance, the noise estimation matrix also includes a plurality of first elements arranged in descending order of distance , The detection device makes a difference between each column element included in the distance-Doppler matrix and the noise estimation matrix, which specifically refers to the distance-Doppler matrix and the noise estimation matrix, respectively corresponding to the same distance The Doppler velocity and the noise estimate are the difference.

Way 2

In the case that the noise estimation matrix is a column matrix, and the detection device determines that the number of the first element included in the noise estimation matrix and the second element included in any column of the distance-Doppler matrix are equal Next, at this time, the distance-Doppler matrix is a two-dimensional matrix, and the noise estimation matrix is a one-dimensional matrix. The detection device can copy the noise estimation matrix, so that the matrix dimension of the copied noise estimation matrix is the same as the matrix dimension of the distance-Doppler matrix.

Specifically, that the matrix dimension of the copied noise estimation matrix is the same as the matrix dimension of the distance-Doppler matrix means that the number of rows included in the copied noise estimation matrix and the distance-Doppler matrix include The number of rows of is equal, and the number of columns included in the copied noise estimation matrix is equal to the number of columns included in the distance-Doppler matrix.

The following is a description with a specific example: in order to improve the efficiency of the difference between the distance-Doppler matrix and the noise estimation matrix, refer to FIG. 7, where 701 shown in FIG. 7 is obtained by the detection device As shown in FIG. 7, taking the noise estimation matrix as a column matrix including 8 elements as an example, the detection device can copy the noise estimation matrix to form a copied noise estimation matrix 702. Specifically, each element included in any row of the copied noise estimation matrix has the same element value.

In the case that the matrix dimensions of the noise estimation matrix and the range-Doppler matrix are the same, the detection device can directly make the difference between the noise estimation matrix and the range-Doppler matrix to obtain the target difference.

It should be clear that this example takes the noise estimation matrix as a column matrix as an example to illustrate. In other examples, the noise estimation matrix may also be a row matrix. When the noise estimation matrix is a row matrix, the distance -Please refer to the above description for the description of the difference between the Doppler matrix and the noise estimation matrix, and the details will not be repeated.

Way 3

In Mode 1 and Mode 2, the first sampling rate is 2 and the second sampling rate is 1 as an example, so that the number of elements included in the noise estimation matrix and any column of the distance-Doppler matrix are included The number of elements in is the same, and in this manner, when the first sampling rate and the second sampling rate are different from the above manner, the number of elements included in the noise estimation matrix and the distance-Dopp The number of elements included in any column of the Le matrix is different.

If the number of first elements included in the noise estimation matrix is greater than the number of second elements included in any column of the distance-Doppler matrix, the detection device reduces the noise estimation matrix to make the noise estimation matrix The number of elements included is equal to the number of elements included in any column of the distance-Doppler matrix.

If the number of first elements included in the noise estimation matrix is less than the number of elements included in any column of the distance-Doppler matrix, the detection device expands the noise estimation matrix so that the noise estimation matrix includes The number of first elements of is equal to the number of second elements included in any column of the distance-Doppler matrix.

In the case that the number of first elements included in the noise estimation matrix is equal to the number of second elements included in any column of the distance-Doppler matrix, the detection device can copy the noise estimation matrix to generate a copy For the subsequent noise estimation matrix, please refer to the above for the specific copying process, so I won’t go into details.

For the description of the method of making the difference between the distance-Doppler matrix and the noise estimation matrix, please refer to the method 2 for details, and the details are not repeated.

Step 312: The detection device determines whether the target difference corresponding to the target second element is greater than or equal to the noise threshold, if not, step 313 is executed, and if yes, step 314 is executed.

In the case that the detection device obtains the target difference value corresponding to the target second element, the detection device can determine whether the target difference value is greater than or equal to the noise threshold. This embodiment does not limit the specific value of the noise threshold. As long as the target difference corresponding to the second element of the target is greater than or equal to the noise threshold, it means that the second element of the target is to be tracked that needs to be tracked. Object can be. If the target difference corresponding to the second element of the target is less than the noise threshold, it means that the second element of the target is an object not to be tracked, and the target object is more likely to be noise.

Optionally, the detection device may obtain the noise threshold in advance according to the false alarm probability, where the false alarm probability refers to the fact that there is no object to be tracked but it is judged to be tracked due to the ubiquitous presence and fluctuation of noise during the radar detection process. The probability of the object.

The following describes the process of determining the noise threshold: it should be clarified that the description of the method for determining the noise threshold in this embodiment is an optional example and is not limited:

Optionally, the detection device may determine a noise threshold for the distance-Doppler matrix, such as selecting any second element in the distance-Doppler matrix, and determining the noise threshold corresponding to the second element , And use the noise threshold for the noise estimation of all the second elements of the range-Doppler matrix.

Optionally, the detection device may respectively determine a corresponding noise threshold for each second element in the distance-Doppler matrix. The following is an example of how the detection device determines the corresponding noise threshold for the target second element:

The detection device can calculate the noise threshold corresponding to the target second element through the following formula:

Noise threshold=TC-30log ^R , where TC is a preset constant, and R is the distance corresponding to the second element of the target in the distance-Doppler matrix.

Step 313: The detection device determines that the target second element is not an object to be tracked.

Specifically, when the detection device determines that the target difference corresponding to the second element of the target is less than the noise threshold, it means that the second element of the target is not a non-tracking object that needs to be tracked, that is, the detection device does not need to track the target The second element is tracked.

Step 314: The detection device determines that the target second element is the object to be tracked.

Specifically, when the detection device determines that the target difference corresponding to the second element of the target is greater than or equal to the noise threshold, it is indicated that the second element of the target is the detected object to be tracked.

Step 315: The detection device tracks the object to be tracked.

Specifically, in the case that the detection device acquires all the objects to be tracked included in the range-Doppler matrix, the detection device acquires the distance and the distance corresponding to all the objects to be tracked included in the distance-Doppler matrix. Puller speed, the third detecting means FTT processing for all objects to be tracked are range and Doppler velocity corresponding to each angle to obtain an object to be tracked.

The detection device can determine the point cloud data, where the point cloud data includes the distance, Doppler velocity, and angle corresponding to all the objects to be tracked included in the distance-Doppler matrix. The detection device can realize the tracking of each object to be tracked according to the distance, Doppler speed and angle of each object to be tracked.

It can be seen that, based on the method shown in this embodiment, the object to be tracked can be determined directly according to the range-Doppler matrix and the noise estimation matrix. Compared with the sliding window CFAR detection method in the process of detecting the object to be tracked, the efficiency of detecting the object to be tracked is effectively improved.

Moreover, with the method shown in this embodiment, the noise estimation matrix can be determined according to the target two-dimensional matrix, and the range-Doppler matrix can realize the detection of the object to be tracked according to the noise estimation matrix without the need to judge the range-Doppler matrix The size of the parameter corresponding to the adjacent element of the object to be tracked is used to detect the object to be tracked. That is, in the process of detecting the object to be tracked in the method shown in this embodiment, when judging whether an element in the distance-Doppler matrix is the object to be tracked, there is no need to judge based on other elements adjacent to the element, and The difference between the second element value of the element and the element value of the first element of the noise estimation matrix can directly determine whether the second element is the object to be tracked, so that the method shown in this embodiment does not The emergence of a situation in which the object to be tracked strongly conceals the object to be tracked weakly. In the method shown in this embodiment, even if there are multiple adjacent objects to be tracked in the range-Doppler matrix, the detection device can accurately detect the objects to be tracked, which improves the accuracy of detecting the objects to be tracked. .

Based on the detection method shown in Figure 3, the following Figure 8 illustrates how to effectively improve the efficiency and accuracy of detecting the object to be tracked:

Step 801: The detection device obtains the signals to be measured received by each receiving antenna of the radar.

In this embodiment, in order to improve the accuracy of detecting the object to be tracked, before performing the process of detecting the object to be tracked, the detection device needs to detect whether there is an interference signal that interferes with the radar. Only when the detection device determines Only when there is no jamming signal that causes interference to the radar, the detection device can realize the accurate detection of the object to be tracked. The following first explains the jamming signal:

Generally, there are two parameters that affect the accuracy of the detection device to detect the object to be tracked, one is noise, and the other is interference. The usual source of noise is the thermal noise of the various components of the radar. In the embodiment shown in Figure 3, if the target spectrum is obtained from the complex signal, the noise included in the complex signal will be evenly distributed in the positive and negative frequencies. On the axis. The interference signal is different from noise. The interference usually comes from the electromagnetic wave signal emitted by the radar on other vehicles. The typical situation is the electromagnetic wave signal emitted by the radar installed on the vehicle on the opposite side to the radar of the vehicle. The electromagnetic wave signal sent to the vehicle is not an echo signal formed by the reflection of the electromagnetic wave emitted by the vehicle’s radar by a real object. Receiving such a signal will cause interference to the subsequent processing of the vehicle’s radar. In this embodiment, the electromagnetic wave signal emitted by the radar on another vehicle is defined as an interference signal, and the frequency band, frequency modulation slope, etc. of the interference signal are different from those of the own vehicle's radar.

The detection device shown in this embodiment is to detect whether the radar of the vehicle currently receives an interference signal, then the detection device can control the radar not to send a detection signal to the surrounding environment, at this time the receiving antenna i of the radar is in the open state, namely The radar receives the signal to be measured from the surrounding environment through the receiving antenna i. If the detection device determines that the signal to be measured is an interference signal, the detection device can determine that the radar is interfered. The receiving antenna i shown in this embodiment For specific instructions, please refer to the embodiment shown in Fig. 3, and details are not repeated.

Step 802: The detection device obtains the spectrum to be measured of the signal to be measured.

For the specific process of the detection device acquiring the spectrum to be measured of the signal to be measured received by the receiving antenna i, please refer to step 301 to step 305 shown in FIG. 3 for the detection device to acquire the echo signal received by the receiving antenna i The specific process of the frequency spectrum is not described in detail.

Step 803: The detection device judges whether the amplitude corresponding to any negative frequency of the negative frequency axis of the spectrum to be measured of the signal to be measured is less than or equal to the interference threshold, and if so, execute step 804.

In the process in which the detection device acquires the spectrum to be measured, this embodiment uses the detection device to sample the complex signal corresponding to the receiving antenna i with a sampling rate of 2 as an example for exemplification. Then the detection device The acquired target spectrum can be seen in Fig. 4b, and the details will not be described in detail.

In this embodiment, if the detection device determines that the amplitude corresponding to any negative frequency included in the negative frequency axis of the spectrum to be measured is less than or equal to the interference threshold, it means that the receiving antenna i has not received the interference signal, and if the detection device It is determined that the amplitude corresponding to any negative frequency included in the negative frequency axis of the spectrum to be measured is less than the interference threshold, which indicates that the receiving antenna i has received the interference signal. The presence of the interference signal will seriously reduce the accuracy of the detection device for detecting the object to be tracked.

In this embodiment, if the radar of the vehicle is not interfered, the waveform of the spectrum to be measured corresponding to the receiving antenna i of the radar of the vehicle can be as shown in Figure 4b, that is, there is no abnormal high energy in the spectrum to be measured, which is specifically reflected in The amplitude corresponding to any negative frequency on the negative frequency axis of the target spectrum is less than or equal to the interference threshold.

If the radar of the vehicle is interfered, there is abnormally high energy in the negative frequency axis of the spectrum to be measured corresponding to the receiving antenna i of the radar of the vehicle, which is embodied in that there are one or more negative frequencies on the negative frequency axis of the spectrum to be measured The corresponding amplitudes are all greater than or equal to the interference threshold.

It can be seen that when the detection device detects that the negative frequency axis of the spectrum to be measured includes one or more negative frequencies and the corresponding amplitude is greater than the interference threshold, the detection device determines that the receiving antenna i of the radar receives the interference signal. If the detection device detects that the amplitudes corresponding to all negative frequencies included in the spectrum to be measured are less than or equal to the interference threshold, the detection device determines that the radar receiving antenna i does not receive the interference signal, and triggers the execution of step 804 .

Step 804: The detection device obtains the time-frequency signal from the radar.

Step 805: The detection device converts the time-frequency signal into a complex signal.

Step 806: The detection device performs over-sampling on each complex signal to obtain sampled data.

Step 807: The detection device performs first-dimensional FFT processing on the sampled data to obtain a frequency spectrum.

Step 808: The detection device obtains a two-dimensional matrix corresponding to each receiving antenna.

For the process shown in step 804 to step 808 in this embodiment, please refer to step 301 to step 306 in Fig. 3 for details, and details are not described in detail.

Step 809: The detection device performs a second-dimensional FFT on the multiple two-dimensional matrices to obtain multiple distance-Doppler matrices.

In the case that the detection device obtains a two-dimensional matrix for each receiving antenna of the radar, the detection device may perform a second-dimensional FFT for each two-dimensional matrix to obtain the distance corresponding to each receiving antenna- Doppler matrix. For a specific description of the distance-Doppler matrix, please refer to the above-mentioned embodiment for details, and the details will not be repeated.

Step 810: The detection device determines the target distance-Doppler matrix.

In this embodiment, in the case that the detection device obtains multiple range-Doppler matrices respectively corresponding to all the receiving antennas of the radar, the detection device can determine the target distance-doppler matrix according to the multiple range-Doppler matrices. Puller matrix. For the description of determining the target distance-Doppler matrix in multiple distance-Doppler matrices shown in this embodiment, refer to step 307 shown in FIG. 3, determining the target two-dimensional matrix from the multiple two-dimensional matrices The specific process is not described in detail in this embodiment.

Step 811: The detection device determines at least one target distance in the target distance-Doppler matrix.

For a specific description of step 811 shown in this embodiment, reference may be made to step 308 shown in FIG. 3, and details are not repeated here. The difference between step 811 and step 308 is: in FIG. 3, the target distance corresponding to the target distance in the target two-dimensional matrix is the amplitude, and as shown in this step 811, the target distance-Doppler matrix corresponding to the target distance is Doppler velocity.

Step 812: The detection device determines a noise estimation matrix.

In this embodiment, the detection device determines the target Doppler velocity according to the target distance in the determined target distance-Doppler matrix. The detection device can determine that an element value of a first element included in the noise estimation matrix is the target Doppler velocity. When the detection device determines multiple target Doppler velocities, the element values of the multiple first elements included in the noise estimation matrix are multiple target Doppler velocities, respectively. For a specific description of the process of the detection device determining the target Doppler velocity according to the target distance shown in this step, please refer to the process of determining the target amplitude by the detection device according to the target distance shown in step 309, which will not be described in detail.

Step 813: The detection device determines the range-Doppler matrix.

Step 814: The detection device makes a difference between the range-Doppler matrix and the noise estimation matrix to obtain the target difference.

Step 815: The detection device determines whether the target difference corresponding to the second element of the target is greater than or equal to the noise threshold, if not, step 816 is performed, and if yes, step 817 is performed.

Step 816: The detection device determines that the target second element is an object not to be tracked.

Step 817: The detection device determines that the second element of the target is the object to be tracked.

Step 818: The detection device tracks the object to be tracked.

For the specific execution process of step 814 to step 818 shown in this embodiment, please refer to step 310 to step 315 shown in FIG. 3 for details, and the specific execution process will not be described in detail.

Using the method shown in this embodiment, the detection device will detect the object to be tracked when there is no interference signal that interferes with the radar, thereby avoiding the presence of the interference signal. The occurrence of a situation where the detection device performs wrong detection of the object to be tracked effectively improves the accuracy of detecting the object to be tracked. As shown in this embodiment, processing is performed according to the distance-Doppler matrix generated by the first echo signal to obtain the noise estimation matrix, so that the element value of the first element included in the obtained noise estimation matrix is closer to the noise , Thereby improving the accuracy of detecting the object to be tracked.

The following Figure 9 illustrates how to effectively improve the robustness of noise estimation:

Step 901: The detection device obtains the signals to be measured received by each receiving antenna of the radar.

Step 902: The detection device obtains the spectrum to be measured of the signal to be measured.

Step 903: The detection device determines whether the amplitude corresponding to any negative frequency of the negative frequency axis of the spectrum to be measured of the signal to be measured is less than or equal to the interference threshold, and if so, execute step 904.

Steps 901 to 903 shown in this embodiment are shown in steps 801 to 803 shown in FIG. 8 for details, and details are not described in detail.

Step 904: The detection device acquires the time-frequency signal from the radar in the first processing period.

In this embodiment, the detection device may predetermine the first processing period for noise estimation, and the detection device only acquires the time-frequency signal from the radar during the duration of the first processing period. This embodiment does not limit the duration of the first processing cycle.

Step 905: The detection device converts the time-frequency signal into a complex signal.

Step 906: The detection device performs over-sampling on each complex signal to obtain sampled data.

Step 907: The detection device performs first-dimensional FFT processing on the sampled data to obtain a frequency spectrum.

Step 908: The detection device obtains a two-dimensional matrix corresponding to each receiving antenna.

Step 909: The detection device performs a second-dimensional FFT on the multiple two-dimensional matrices to obtain multiple range-Doppler matrices.

Step 910: The detection device determines the target distance-Doppler matrix.

Step 911: The detection device determines at least one target distance in the target distance-Doppler matrix.

Step 912: The detection device determines a noise estimation matrix.

For the specific execution process of step 904 to step 912 shown in this embodiment, please refer to step 801 to step 812 shown in FIG. 8, and the specific execution process will not be repeated in this embodiment.

Step 913: The detection device determines the distance-Doppler matrix in the second processing period.

In this embodiment, the detection device may be preset with a second processing period, and the detection device only determines the range-Doppler matrix according to the second echo signal in the second processing period. If the detection device determines that the timing of the second processing period is exceeded, the detection device may return to step 901 or return to step 904 to re-acquire the noise estimation matrix.

This embodiment does not limit the specific duration of the first processing period and the second processing period, as long as the processing timing of the first processing period is earlier than the processing timing of the second processing period in terms of processing timing. This embodiment does not limit the specific correspondence between the first processing period and the second processing period, as long as one first processing period corresponds to one or more second processing periods.

For example, the first processing period and the second processing period may have a one-to-one correspondence, that is, the noise estimation matrix acquired by the detection device in the first processing period is used to perform the second processing on the detection device. Noise estimation is performed on the time-frequency signal obtained by the period.

For another example, the first processing period and the second processing period may have a one-to-many correspondence relationship, that is, the noise estimation matrix acquired by the detection device in the first processing period is used for the subsequent multiplication of the detection device. Noise estimation is performed on the time-frequency signals acquired in the second processing cycle respectively.

Step 914: The detection device makes a difference between the range-Doppler matrix and the noise estimation matrix to obtain a target difference value.

Step 915: The detection device determines whether the target difference corresponding to the target second element is greater than or equal to the noise threshold, if not, step 916 is executed, and if yes, step 917 is executed.

Step 916: The detection device determines that the target second element is an object not to be tracked.

Step 917: The detection device determines that the target second element is the object to be tracked.

Step 918: The detection device tracks the object to be tracked in the second processing cycle.

For the specific process shown in step 913 to step 918 shown in this embodiment, please refer to step 813 to step 818 shown in FIG. 8 for details, and the specific execution process will not be described in detail.

Because the noise of the radar usually changes with the temperature of the radar, and the temperature of the radar is usually related to the working time of the radar. Therefore, in the working process of the radar, the duration of the first processing period and the second processing period can be allocated in a certain proportion, so as to ensure the robustness of the noise estimation matrix estimated by the processor.

The foregoing embodiment provides a detailed description of the detection method provided by the present application, and the following describes the structure of the detection device for executing the detection method shown in the foregoing embodiment:

Refer to FIG. 10, which is a structural example diagram of an embodiment of the detection device provided by the present invention. The detection device 1000 shown in this embodiment includes an acquisition unit 1001 and a processing unit 1002.

The obtaining unit 1001 is configured to obtain a frequency spectrum corresponding to the first echo signal received by the radar, and the negative frequency axis of the frequency spectrum is composed of the frequency of the noise and the corresponding amplitude;

The processing unit 1002 is configured to determine a noise estimation matrix according to the negative frequency axis of the frequency spectrum, and the noise estimation matrix includes an element taking the value of the amplitude or the Doppler velocity converted from the amplitude; according to the first echo signal Or the second echo signal received by the radar obtains the range-Doppler matrix; among the multiple elements of the range-Doppler matrix, the difference between an element value and an element value in the noise estimation matrix is greater than or An element equal to the noise threshold is determined as the object to be tracked.

Optionally, the obtaining unit 1001 is specifically configured to:

Acquiring a complex signal, where the complex signal is converted from the first echo signal;

Over-sampling the complex signal to obtain sampled data;

Perform a first-dimensional fast Fourier transform FFT on the sampled data to obtain the frequency spectrum.

Optionally, the radar has a plurality of receiving antennas, and each of the receiving antennas is used to receive a plurality of the first echo signals, and the processing unit 1002 determines the noise estimation matrix according to at least part of the negative frequency of the negative frequency axis of the spectrum In the process, specifically used for:

Acquiring a plurality of initial two-dimensional matrices, the plurality of initial two-dimensional matrices respectively corresponding to the plurality of receiving antennas, the initial two-dimensional matrix including the corresponding frequency spectrum of the first echo signal received by the receiving antenna;

Determining a target two-dimensional matrix, where the target two-dimensional matrix is one of the plurality of initial two-dimensional matrices, or, the target two-dimensional matrix is formed by superimposing the plurality of initial two-dimensional matrices;

The noise estimation matrix is determined according to the target two-dimensional matrix.

Optionally, in the process of determining the noise estimation matrix according to the target two-dimensional matrix, the processing unit is specifically configured to:

Determine the target distance in the target two-dimensional matrix, where the target distance is converted according to the negative frequency included in the frequency spectrum;

Determine the target amplitude corresponding to the target distance in the target two-dimensional matrix;

The noise estimation matrix is determined, and the noise estimation matrix includes an element whose value is the target amplitude.

Optionally, in the process of determining the target amplitude corresponding to the target distance in the target two-dimensional matrix, the processing unit 1002 is specifically configured to:

Average the respective amplitudes of the target distance in the target two-dimensional matrix to obtain the target amplitude.

Optionally, the processing unit 1002 is further used for:

Performing a second-dimensional FFT on the multiple initial two-dimensional matrices to obtain multiple initial distance-Doppler matrices;

Determine the target distance-Doppler matrix, where the target distance-Doppler matrix is one of the multiple initial distance-Doppler matrices, or, the target distance-Doppler matrix is the multiple initial distance- Doppler matrix is superimposed;

The noise estimation matrix is determined according to the target distance-Doppler matrix.

Optionally, in the process of determining the noise estimation matrix according to the target distance-Doppler matrix, the processing unit 1002 is specifically used for:

Determine the target distance in the target distance-Doppler matrix, where the target distance is converted according to the negative frequency included in the frequency spectrum;

Determine the target Doppler velocity corresponding to the target distance in the target distance-Doppler matrix;

The noise estimation matrix is determined, and the noise estimation matrix includes an element whose value is the target Doppler velocity.

Optionally, when the processing unit 1002 determines the Doppler velocity corresponding to the target distance in the target distance-Doppler matrix, the processing unit 1002 is specifically configured to:

The Doppler velocity corresponding to the target distance in the target distance-Doppler matrix is averaged to obtain the target Doppler velocity.

Optionally, the processing unit 1002 is further used for:

When the dimension of the noise estimation matrix is not equal to the dimension of the distance-Doppler matrix, the dimension of the noise estimation matrix is processed, and the dimension of the noise estimation matrix after processing is equal to the distance-doppler matrix. The dimensions of the Puller matrices are equal.

Optionally, the obtaining unit 1001 is also used for:

Acquiring a complex signal to be tested, the complex signal to be tested is converted from the signal to be tested;

Over-sampling the data signal to be tested to obtain the sampled data to be tested;

Perform a first-dimensional FFT on the sampled data to be measured to obtain the spectrum to be measured;

It is determined that the amplitude corresponding to any negative frequency included in the negative frequency axis of the spectrum to be measured is less than or equal to the interference threshold.

In one implementation manner, the detection device 1000 may be installed in a radar, and in another implementation manner, the detection device 1000 may also be installed independently of the radar. The corresponding units included in the detection device 1000 are respectively used to execute the corresponding operations and/or processing performed by the detection device in each method embodiment.

In an implementation manner, the module for performing the transceiving function in the acquiring unit 1001 included in the detection device 1000 may be a transceiver, and the module for performing processing functions in the acquiring unit 1001 and the processing unit 1002 may be a processor. Among them, the transceiver has the function of sending and/or receiving, and the transceiver can also be replaced by a receiver and/or transmitter.

In another implementation manner, the detection device 1000 may be a chip or an integrated circuit. At this time, the acquiring unit 1001 and the processing unit 1002 may be logic circuits.

In an implementation manner, the processing unit 1002 may be a processing device, and the functions of the processing device may be partially or fully implemented by software.

Optionally, the functions of the processing device may be partially or fully implemented by software. At this time, the processing device may include a memory and a processor, where the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform corresponding processing and/or steps in any method embodiment.

Optionally, the processing device may only include a processor. The memory for storing the computer program is located outside the processing device, and the processor is connected to the memory through a circuit/wire to read and execute the computer program stored in the memory.

Optionally, the functions of the processing device may be partially or fully implemented by hardware. At this time, the processing device may include an input interface circuit, a logic circuit, and an output interface circuit.

For example, the processing device may be one or more field-programmable gate arrays (FPGA), application specific integrated circuit (ASIC), system on chip (SoC), and central processing unit. (central processor unit, CPU), network processor (NP), digital signal processing circuit (digital signal processor, DSP), microcontroller (microcontroller unit, MCU), programmable controller (programmable logic device, PLD) or other integrated chips, or any combination of the above chips or processors.

In addition, this application also provides an electronic device, which will be described below in conjunction with FIG. 11:

Referring to FIG. 11, FIG. 11 is a structural example diagram of an embodiment of an electronic device provided by the present invention. As shown in FIG. 11, the electronic device 1100 includes a processor 1101, a transceiver 1102, and a memory 1003. Among them, the processor 1101, the transceiver 1102, and the memory 1103 can communicate with each other through an internal connection path to transfer control signals and/or data signals. The memory 1103 is used to store a computer program, and the processor 1101 is used to call and run the computer program from the memory 1103 to control the transceiver 1102 to send and receive signals.

Optionally, the processor 1101 and the memory 1103 may be combined into one processing device, and the processor 1101 is configured to execute the program code stored in the memory 1103 to implement the foregoing functions.

Optionally, the memory 1103 may also be integrated in the processor 1101. Alternatively, the memory 1103 is independent of the processor 1101, that is, located outside the processor 1101.

The processor 1101 may be used to execute the actions implemented by the detection device described in the foregoing method embodiments. The transceiver 1102 may be used to perform receiving or sending actions performed by the detection device, and the memory 1103 is used to implement a storage function.

Optionally, the electronic device 1100 may further include a power supply 1105 for providing power to various devices or circuits in the electronic device 1100.

In addition, in order to make the functions of the electronic device 1100 more complete, the electronic device 1100 may further include one or more of the input unit 1106, the display unit 1107, the sensor 1110, and the like.

Optionally, the input unit 1106 may be a signal input interface, and the display unit 1107 may also be a signal output interface.

In addition, this application also provides a detection system, including the detection device and radar in each method embodiment of this application.

The present application also provides a computer-readable storage medium with a computer program stored on the computer-readable storage medium. When the computer program is executed by the computer, the computer executes the operations performed by the detection device in any method embodiment and/or deal with.

The present application also provides a computer program product. The computer program product includes computer program code. When the computer program code is run on a computer, the computer can execute the operation and/or processing performed by the detection device in any method embodiment.

The application also provides a chip including a processor. The memory for storing the computer program is provided independently of the chip, and the processor is used to execute the computer program stored in the memory to execute the operation and/or processing performed by the detection device in any method embodiment.

Further, the chip may also include a memory and/or a communication interface. The communication interface can be an input/output interface, an input/output circuit, etc.

The processor mentioned in the above embodiments may be an integrated circuit chip, which has the ability to process signals. In the implementation process, the steps of the foregoing method embodiments can be completed by hardware integrated logic circuits in the processor or instructions in the form of software. The processor can be a general-purpose processor, digital signal processor (digital signal processor, DSP), application specific integrated circuit (ASIC), field programmable gate array (field programmable gate array, FPGA) or other programmable logic Devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like. The steps of the method disclosed in the embodiments of the present application may be directly embodied as being executed and completed by a hardware encoding processor, or executed and completed by a combination of hardware and software modules in the encoding processor. The software module can be located in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

Those skilled in the art can clearly understand that, for the convenience and conciseness of the description, the specific working process of the above-described system, device, and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, device, and method may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the unit is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or may be Integrate into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.

The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or may also be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present invention essentially or the part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium. , Including several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method in each embodiment of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes. .

Above, the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, a person of ordinary skill in the art should understand that: The technical solutions recorded in the embodiments are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

A detection method for detecting an object to be tracked, characterized in that the method includes:

Acquiring a frequency spectrum corresponding to the first echo signal received by the radar, where the negative frequency axis of the frequency spectrum is composed of the frequency of the noise and the corresponding amplitude;

Determining a noise estimation matrix according to the negative frequency axis of the frequency spectrum, the noise estimation matrix including an element taking the value of the amplitude or an element of the Doppler velocity converted from the amplitude;

Acquiring a range-Doppler matrix according to the first echo signal or the second echo signal received by the radar;

Among the multiple elements of the distance-Doppler matrix, an element with a difference between an element value and an element value in the noise estimation matrix greater than or equal to a noise threshold is determined as the object to be tracked.
The detection method according to claim 1, wherein said obtaining the frequency spectrum corresponding to the first echo signal received by the radar comprises:

Acquiring a complex signal, where the complex signal is converted from the first echo signal;

Over-sampling the complex signal to obtain sampled data;

Perform a first-dimensional fast Fourier transform FFT on the sampled data to obtain the frequency spectrum.
The detection method according to claim 1 or 2, wherein the radar has a plurality of receiving antennas, and each of the receiving antennas is used to receive a plurality of the first echo signals. At least part of the negative frequency axis of the negative frequency axis determines the noise estimation matrix including:

Acquire multiple initial two-dimensional matrices, the multiple initial two-dimensional matrices respectively corresponding to the multiple receiving antennas, and the initial two-dimensional matrix includes the corresponding data of the first echo signal received by the receiving antenna The frequency spectrum;

Determining a target two-dimensional matrix, where the target two-dimensional matrix is one of the multiple initial two-dimensional matrices, or the target two-dimensional matrix is formed by superimposing the multiple initial two-dimensional matrices;

The noise estimation matrix is determined according to the target two-dimensional matrix.
The detection method according to claim 3, wherein the determining the noise estimation matrix according to the target two-dimensional matrix comprises:

Determining a target distance in the target two-dimensional matrix, where the target distance is converted according to the negative frequency included in the frequency spectrum;

Determining a target amplitude corresponding to the target distance in the target two-dimensional matrix;

The noise estimation matrix is determined, and the noise estimation matrix includes an element whose value is the target amplitude.
The detection method according to claim 4, wherein the determining the target amplitude corresponding to the target distance in the target two-dimensional matrix comprises:

Average the respective amplitudes of the target distance in the target two-dimensional matrix to obtain the target amplitude.
The detection method according to claim 3, wherein after said acquiring a plurality of initial two-dimensional matrices, the method further comprises:

Performing a second-dimensional FFT on the multiple initial two-dimensional matrices to obtain multiple initial distance-Doppler matrices;

Determine a target distance-Doppler matrix, where the target distance-Doppler matrix is one of the multiple initial distance-Doppler matrices, or the target distance-Doppler matrix is Two initial distance-Doppler matrices are superimposed;

The noise estimation matrix is determined according to the target distance-Doppler matrix.
The detection method according to claim 6, wherein the determining the noise estimation matrix according to the target distance-Doppler matrix comprises:

Determining a target distance in the target distance-Doppler matrix, where the target distance is converted according to the negative frequency included in the frequency spectrum;

Determining a target Doppler velocity corresponding to the target distance in the target distance-Doppler matrix;

The noise estimation matrix is determined, and the noise estimation matrix includes an element whose value is the target Doppler velocity.
The detection method according to claim 7, wherein the determining the Doppler velocity corresponding to the target distance in the target distance-Doppler matrix comprises:

The Doppler velocities corresponding to the target distance in the target distance-Doppler matrix are averaged to obtain the target Doppler velocity.
The detection method according to any one of claims 1 to 8, wherein before the determining the object to be tracked, the method further comprises:

When the dimensions of the noise estimation matrix and the distance-Doppler matrix are not equal, the dimensions of the noise estimation matrix are processed, and the sum of the dimensions of the processed noise estimation matrix is The dimensions of the distance-Doppler matrix are equal.
The detection method according to any one of claims 1 to 9, characterized in that, before the obtaining the frequency spectrum corresponding to the first echo signal that has been received by the radar, the method further comprises:

Acquiring a signal to be measured from the radar, where the signal to be measured is a signal received by the radar from the surrounding environment;

Acquiring a complex signal to be measured, where the complex signal to be measured is converted from the signal to be measured;

Over-sampling the data signal to be tested to obtain sampled data to be tested;

Performing a first-dimensional FFT on the sampled data to be measured to obtain the spectrum to be measured;

It is determined that the amplitude corresponding to any negative frequency included in the negative frequency axis of the spectrum to be measured is less than or equal to the interference threshold.
A detection device for detecting an object to be tracked, which is characterized in that it comprises:

An acquiring unit, configured to acquire a frequency spectrum corresponding to the first echo signal that has been received by the radar, and the negative frequency axis of the frequency spectrum is composed of the frequency of the noise and the corresponding amplitude;

A processing unit, configured to determine a noise estimation matrix according to the negative frequency axis of the frequency spectrum, the noise estimation matrix including an element taking the value of the amplitude or an element of the Doppler velocity converted from the amplitude; The first echo signal or the second echo signal received by the radar obtains a range-Doppler matrix; among the multiple elements of the range-Doppler matrix, the element value and the noise estimation matrix An element in which the difference of an element value is greater than or equal to the noise threshold is determined as the object to be tracked.
The detection device according to claim 11, wherein the acquiring unit is specifically configured to:

Acquiring a complex signal, where the complex signal is converted from the first echo signal;

Over-sampling the complex signal to obtain sampled data;

Perform a first-dimensional fast Fourier transform FFT on the sampled data to obtain the frequency spectrum.
The detection device according to claim 11 or 12, wherein the radar has a plurality of receiving antennas, and each of the receiving antennas is used to receive a plurality of the first echo signals, then the processing unit is in accordance with In the process of determining at least part of the negative frequencies of the negative frequency axis of the frequency spectrum, the noise estimation matrix is specifically used for:

Acquire multiple initial two-dimensional matrices, the multiple initial two-dimensional matrices respectively corresponding to the multiple receiving antennas, and the initial two-dimensional matrix includes the corresponding data of the first echo signal received by the receiving antenna The frequency spectrum;

Determining a target two-dimensional matrix, where the target two-dimensional matrix is one of the multiple initial two-dimensional matrices, or the target two-dimensional matrix is formed by superimposing the multiple initial two-dimensional matrices;

The noise estimation matrix is determined according to the target two-dimensional matrix.
The detection device according to claim 13, wherein the processing unit is specifically configured to: in the process of determining the noise estimation matrix according to the target two-dimensional matrix:

Determining a target distance in the target two-dimensional matrix, where the target distance is converted according to the negative frequency included in the frequency spectrum;

Determining a target amplitude corresponding to the target distance in the target two-dimensional matrix;

The noise estimation matrix is determined, and the noise estimation matrix includes an element whose value is the target amplitude.
The detection device according to claim 14, wherein the processing unit is specifically configured to: in the process of determining the target amplitude corresponding to the target distance in the target two-dimensional matrix:

Average the respective amplitudes of the target distance in the target two-dimensional matrix to obtain the target amplitude.
The detection device according to claim 13, wherein the processing unit is further configured to:

Performing a second-dimensional FFT on the multiple initial two-dimensional matrices to obtain multiple initial distance-Doppler matrices;

Determine a target distance-Doppler matrix, where the target distance-Doppler matrix is one of the multiple initial distance-Doppler matrices, or the target distance-Doppler matrix is Two initial distance-Doppler matrices are superimposed;

The noise estimation matrix is determined according to the target distance-Doppler matrix.
The detection device according to claim 16, wherein the processing unit is specifically configured to: in the process of determining the noise estimation matrix according to the target distance-Doppler matrix:

Determining a target distance in the target distance-Doppler matrix, where the target distance is converted according to the negative frequency included in the frequency spectrum;

Determining a target Doppler velocity corresponding to the target distance in the target distance-Doppler matrix;

The noise estimation matrix is determined, and the noise estimation matrix includes an element whose value is the target Doppler velocity.
The detection device according to claim 17, wherein the processing unit is specifically configured to: in the process of determining the Doppler velocity corresponding to the target distance in the target distance-Doppler matrix:

The Doppler velocities corresponding to the target distance in the target distance-Doppler matrix are averaged to obtain the target Doppler velocity.
The detection device according to any one of claims 11 to 18, wherein the processing unit is further configured to:

When the dimensions of the noise estimation matrix and the distance-Doppler matrix are not equal, the dimensions of the noise estimation matrix are processed, and the sum of the dimensions of the processed noise estimation matrix is The dimensions of the distance-Doppler matrix are equal.
The detection device according to any one of claims 11 to 19, wherein the acquiring unit is further configured to:

Acquiring a complex signal to be measured, where the complex signal to be measured is converted from the signal to be measured;

Over-sampling the data signal to be tested to obtain sampled data to be tested;

Performing a first-dimensional FFT on the sampled data to be measured to obtain the spectrum to be measured;

It is determined that the amplitude corresponding to any negative frequency included in the negative frequency axis of the spectrum to be measured is less than or equal to the interference threshold.
An electronic device for detecting an object to be tracked, which is characterized by comprising a transceiver, a memory, and a processor. The memory is used to store a computer program, and the processor is used to read and execute the The computer program to perform the method according to any one of claims 1-10.
A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, and when the computer program is executed on a computer, the computer is caused to execute as described in any one of claims 1-10. The method described.