A millimeter wave radar range-azimuth map extraction method and device

Abstract

The application discloses a kind of millimeter wave radar range azimuth chart extraction method and device, comprising: extracting data matrix, the dimension of data matrix along virtual receiving channel is carried out coherent accumulation, then Fourier transform is carried out and takes absolute value and is carried out Doppler peak detection, Doppler peak position is converted into Doppler phase shift, according to the weight of different peak value pks size allocation and sum value calculation, after normalizing sum value, corresponding compensation matrix is constructed according to the size of data matrix;Data matrix is compensated according to compensation matrix, and RA chart is obtained after executing the subsequent step of MVDR algorithm.The application optimizes the process of mvdr, carries out Doppler compensation under multi-target environment, eliminates the influence of target Doppler on RA chart, and the angle precision of the obtained RA chart is higher;Target peak beam is narrower, and the expansion in distance dimension and angle dimension is smaller;Target peak energy value is larger, and signal-to-noise ratio is higher.

Description

Technical Field

[0001] The present invention relates to the field of millimeter wave radar-video fusion, and in particular to a method and device for extracting a millimeter wave radar range and azimuth map. Background Art

[0002] In millimeter-wave radar-video fusion technology, the fusion of sensor data can originate from different locations in the data processing chain. Fusion can be divided into pre-fusion and post-fusion, depending on the data source location and the information contained. Pre-fusion pre-processes and extracts features from the raw data from different sensors to provide better input for the subsequent fusion process. In the millimeter-wave radar signal and data processing chain, different levels of data can be output at different time points. Common data processing chains are as follows:

[0003] Adc signal→Radar cube→Range-Doppler-Azimuth tensor→Point cloud→tracking objects

[0004] In typical post-fusion processing, radar data often comes from point clouds or tracking objects. This input data is relatively low-information and has a high accuracy rate, which places a low ceiling on the fusion algorithm. To obtain richer data during the fusion phase, pre-fusion often employs a Range-Doppler-Azimuth tensor (RA / RD) map. This proposal focuses on RA map extraction methods and quality, and will therefore elaborate on these aspects.

[0005] To obtain the RA map, perform a 1D FFT on each receiving channel and each chirp in the radar cube to obtain the range dimension. These results are then sorted sequentially to form a 1D FFT radar cube. The 1D FFT radar cube is then extracted, following the range bin order, into single-chirp multi-channel vector data or multi-chirp multi-channel matrix data for direction of arrival (DOA) estimation. Common methods include digital beamforming (DBF), minimum variance distortionless response (MVDR), and multiple signal classification algorithm (MUSIC). After performing DOA estimation for each range bin, a Range-Azimuth map (RA map) is obtained. This RA map provides information on the distribution of targets within the radar detection range in both range and horizontal angle, providing a useful input for fusion algorithms with video images.

[0006] Among the commonly used methods, DBF requires multi-channel data of at least one chirp to obtain the RA diagram, but its angular resolution is low, which is basically equivalent to directly performing angular FFT. In addition, since only one chirp's data is used, the process is greatly affected by noise; MVDR and music require data from multiple chirps to construct the data noise covariance matrix. Their angular resolution is higher than DBF, and the target beam width is narrower, which can better distinguish targets adjacent in the angular dimension. MVDR or music algorithms are often used in actual fusion algorithms. Among them, since the music algorithm involves the eigenvalue decomposition of the matrix, it introduces a large amount of calculation and requires prior information on the number of signal sources. Therefore, the MVDR method is more widely used in engineering practice.

[0007] Whether it is MVDR or music, multiple chirp data are needed to construct its data noise covariance matrix, and in order to obtain the data noise covariance matrix better and more accurately, a larger number of chirps are often required, which will introduce the influence of target Doppler, especially in TDM (time division multiplex) or BPM (bi-phase multiplex) modulation modes. Since different virtual receiving channels are generated in different chirps, the corresponding Doppler frequency shift will inevitably be generated due to the target Doppler, resulting in corresponding errors in DoA. Summary of the Invention

[0008] The present invention aims to address the deficiencies of the prior art and propose a method for extracting a millimeter-wave radar range-azimuth map.

[0009] The object of the present invention is achieved through the following technical solution: a method for extracting a millimeter wave radar range and azimuth map, the method comprising:

[0010] S1. Traverse each distance unit in the millimeter wave radar to obtain a data matrix. The size of the data matrix is ​​obtained by the number of virtual receiving channels and the chirp sent by the radar in one frame.

[0011] S2, coherently accumulate the data matrix along the dimension of the virtual receiving channel, then perform Fourier transform of the chirp dimension and take the absolute value;

[0012] S3, performing Doppler peak detection on the sequence processed by S2 to obtain the Doppler peak position;

[0013] S4, converting the Doppler peak position into the Doppler phase shift, assigning weights according to the pks values ​​of different peaks and calculating the sum, normalizing the sum and constructing the corresponding compensation matrix according to the size of the data matrix;

[0014] S5. Compensate the data matrix according to the compensation matrix, calculate the data noise defense difference based on the compensated data matrix, traverse each angle to calculate the steering vector, calculate the response of the MVDR filter at each angle, and integrate the results into the millimeter wave radar range and azimuth map.

[0015] Furthermore, the extraction of the data matrix specifically includes: traversing each distance unit, extracting a data matrix X1 of size Nchannel*Nchirp from the radar data matrix that has undergone fast Fourier transform in the distance dimension, where Nchannel represents the number of virtual receiving channels, and Nchirp represents the number of chirps sent by the radar in one frame.

[0016] Furthermore, the specific steps in S2 are:

[0017]

[0018] X1_fft_abs=abs(fft(X1 array ,Nfft))

[0019] Where X1 is the extracted data matrix, Nchannel represents the number of virtual receiving channels, Nchirp represents the number of chirps sent by the radar in one frame, X1 array is the coherent accumulation result, abs(.) means taking the absolute value;

[0020] Furthermore, the Doppler peak detection includes constant false alarm rate detection or findpeaks function detection. The constant false alarm rate detection is specifically as follows: detecting the target peak, recording all detected peaks and their peak positions idx, recorded as a set pks_location = [peaks location], and the number of peaks is recorded as Npks; the findpeaks function detection is specifically as follows: after the findpeaks function is executed, the peaks and their positions are sorted from large to small according to the peak size, and the top N peaks and their positions are stored in the pks_location set matrix.

[0021] Furthermore, the conversion of the Doppler peak position into the Doppler phase shift is specifically:

[0022]

[0023] where location i is the position corresponding to the i-th peak, and Ntx is the interval between the two values ​​before and after the chirp dimension in the X1 matrix, which is NTx chirp cycles sent by the radar.

[0024] Furthermore, the weights are allocated according to the pks of different peak values ​​and the sum is calculated as follows:

[0025]

[0026] in,

[0027] Furthermore, the construction process of the compensation matrix is ​​specifically as follows:

[0028]

[0029] The resulting Doppler compensation matrix doppler_comp_matrix has a size of Nchannel*Nchirp.

[0030] Furthermore, the compensation of the data matrix according to the compensation matrix is ​​specifically as follows: dot multiplication of the data matrix X1 and the compensation matrix doppler_comp_matrix to obtain the compensated data matrix X1_comp:

[0031] X1_comp=X1·doppler_comp_matrix

[0032] On the other hand, the present specification provides a device for extracting a millimeter-wave radar range and azimuth map, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, it implements the method for extracting a millimeter-wave radar range and azimuth map.

[0033] On the other hand, the present specification provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the method for extracting a millimeter-wave radar range and azimuth map.

[0034] Beneficial effects:

[0035] This invention optimizes the MVDR process to perform Doppler compensation in a multi-target environment, eliminating the effects of target Doppler on the RA diagram, such as angle estimation errors and target size expansion on the RA diagram. The resulting RA diagram has higher angle accuracy, a narrower target peak beam, and less expansion in the range and angle dimensions. It also has a higher target peak energy value and a higher signal-to-noise ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flow chart of a method for extracting a millimeter-wave radar range-azimuth map provided by an embodiment of the present invention;

[0037] Figure 2 is the RA diagram obtained using conventional methods;

[0038] Figure 3 RA graph obtained by the method provided in an embodiment of the present invention;

[0039] Figure 4 A schematic diagram of a device for extracting a millimeter-wave radar range-azimuth map provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The specific embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0041] like Figure 1 As shown, a method for extracting a millimeter-wave radar range-azimuth map includes the following specific steps:

[0042] S1. Traverse each distance unit in the millimeter wave radar to obtain a data matrix. The size of the data matrix is ​​obtained by the number of virtual receiving channels and the chirp sent by the radar in one frame.

[0043] Specifically, in this embodiment, this step includes: extracting the data matrix X1: traversing each range bin (distance unit), extracting the data matrix X1 of size Nchannel*Nchirp from the 1dfft radarcube (radar data matrix that has undergone fast Fourier transform in the distance dimension), where Nchannel represents the number of virtual receiving channels (if there are 3 Txs, 4 Rxs, and time division modulation is performed, there are a total of 3*4=12 virtual receiving channels), and Nchirp represents the number of chirps sent by the radar in one frame (the waveform sent once by the fmcw millimeter wave radar is called a chirp).

[0044] S2. Coherently accumulate the data matrix along the dimension of the virtual receiving channel, then perform Fourier transform on the chirp dimension and take the absolute value to obtain the Doppler spectrum absolute value sequence;

[0045] Specifically, in this embodiment, this step includes: performing coherent accumulation on X1 along the channel dimension, then performing FFT on the chirp dimension, taking the absolute value of the result, and obtaining a Doppler dimension spectrum absolute value sequence X1_fft_abs.

[0046]

[0047] X1_fft_abs=abs(fft(X1 array ,Nfft))

[0048] Where Nfft represents the number of Doppler fast Fourier transform points, which is generally consistent with the Nchirp number, or is the smallest power of 2 (or 4, depending on whether the fast Fourier transform is radix 2 or radix 4) greater than Nchirp.

[0049] S3, performing Doppler peak detection on the Doppler spectrum absolute value sequence to obtain the Doppler peak position;

[0050] Specifically, in this embodiment, this step includes performing CFAR (Constant False Alarm Rate Detector) detection on X1_fft_abs to detect target peaks, recording all detected peaks and their peak locations idx, denoted as the set pks_location = [peaks location], and denoting the number of peaks as Npks. Since CFAR is a commonly used algorithm, it is used here to detect peaks in the X1_fft_abs signal and is not described in detail.

[0051] Alternatively, the findpeaks function (which searches for peaks and detects whether a value in the sequence is greater than the two values ​​before and after it; if so, it is detected as a peak) can be used instead of CFAR. After the findpeaks function is executed, the peaks and their positions are sorted from largest to smallest by peak size, and the top N peaks and their positions are stored in the pks_location matrix. The number N can be selected based on experience, generally 1 or 2. If it is 1, it means that the same-distance and same-speed conditions in the radar target are ignored. Where pks refers to the peak size contained in the Doppler spectrum absolute value sequence obtained after the findpeaks function is executed, location represents the peak position index, and pks is the peak intensity (amplitude) corresponding to each index value.

[0052] pks_location=[peaks location]

[0053] peaks={pks1,pks2,…pks Npks}

[0054] location={location1,location2,…location Npks}

[0055] S4, converting the Doppler peak position into a Doppler phase shift, assigning weights according to the pks values ​​of different peaks and calculating the sum to obtain a Doppler peak phase compensation value, normalizing the Doppler peak phase compensation value and constructing a corresponding compensation matrix according to the size of the data matrix;

[0056] Specifically, in this embodiment, the step includes: converting the Doppler peak position into a Doppler phase shift:

[0057]

[0058] The denominator in the formula for calculating phase shift includes Ntx. This is because in TDM modulation mode, Tx is transmitted in time, while X1 is virtualized into multiple virtual receiving channels. Therefore, the two values ​​before and after the chirp dimension in the X1 matrix are actually separated by NTx radar chirp cycles, so it is necessary to divide by Ntx. After obtaining the phase shift caused by each Doppler peak, weights are assigned according to the pks value of different peaks and their sum is calculated as follows:

[0059]

[0060] Normalize pks_phase_comp as follows:

[0061]

[0062] comp_fai is the phase value of the normalized Doppler-phase compensation value doppler_comp

[0063] After normalization, the corresponding compensation matrix doppler_comp_matrix is ​​constructed according to the size of X1 as follows:

[0064]

[0065] The resulting Doppler compensation matrix doppler_comp_matrix is ​​of size Nchannel*Nchirp, where doppler_comp_array is the compensation array, NRx is the number of receiving antenna units, and NTx*NRx is Nchannel, the number of receiving channels.

[0066] S5. Compensate the data matrix according to the compensation matrix, calculate the data noise defense difference based on the compensated data matrix, traverse each angle to calculate the steering vector, calculate the response of the MVDR filter at each angle, and integrate the results into the millimeter wave radar range and azimuth map.

[0067] Specifically, compensating the data matrix according to the compensation matrix includes: performing a dot product of X1 and doppler_comp_matrix to obtain the compensated X1_comp:

[0068] X1_comp=X1·doppler_comp_matrix

[0069] After compensation, the subsequent steps of the MVDR algorithm are executed to produce an RA graph. The MVDR subsequent steps include calculating the data noise covariance R using X1_comp, calculating the steering vector SteeringVector at each angle, calculating the MVDR filter response at each angle, and integrating the results into an RA graph of size Nrangebin * Nazimuthstep, where Nrangebin is the number of radar range bins and Nazimuthstep is the number of angles traversed in this step. Since the MVDR subsequent steps are common processing methods, they are not detailed here.

[0070] When using common methods such as MVDR and MUSIC to generate RA diagrams, errors caused by target Doppler will be introduced because they need to obtain data from multiple chirps to construct the data noise covariance matrix.

[0071] Simulation is used to illustrate the difference between the RA diagram generated by the proposed method and the RA diagram generated by the conventional method. The simulation conditions are:

[0072]

[0073]

[0074] Set up two point targets, and their point cloud information is:

[0075] Target 1 Target 2 Distance (m) 50m 60m Doppler (m / s) 1.3950 m / s 4.1850 m / s Horizontal angle (deg) 12.7311° -12.7311°

[0076] The RA diagram obtained by conventional methods is as follows Figure 2 As shown:

[0077] The range and azimuth angles corresponding to the two target peaks are:

[0078] Target 1 Target 2 Distance (m) 50.7031m 60.1563m Horizontal angle (deg) 14° -10°

[0079] Using the method described in this proposal, after Doppler compensation, the RA diagram is obtained as follows Figure 3 As shown:

[0080] The range and azimuth angles corresponding to the two target peaks are:

[0081] Target 1 Target 2 Distance (m) 49.8438m 60.1563m Horizontal angle (deg) 13° -13°

[0082] Corresponding to the aforementioned embodiment of a method for extracting a millimeter-wave radar range-azimuth map, the present invention also provides an embodiment of a device for extracting a millimeter-wave radar range-azimuth map.

[0083] See also Figure 4 An embodiment of the present invention provides a device for extracting a millimeter-wave radar range-azimuth map, comprising a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it is used to implement a method for extracting a millimeter-wave radar range-azimuth map in the above embodiment.

[0084] The embodiment of the device for extracting the range and azimuth map of a millimeter-wave radar provided by the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capabilities in which it is located reading the corresponding computer program instructions in the non-volatile memory into the memory for execution. From the hardware level, if Figure 4 As shown, it is a hardware structure diagram of any device with data processing capability where the millimeter wave radar range and azimuth map extraction device provided by the present invention is located, except Figure 4In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0085] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0086] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present invention. A person of ordinary skill in the art can understand and implement the present invention without inventive work.

[0087] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the method for extracting a millimeter-wave radar range and azimuth map in the above embodiment is implemented.

[0088] The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the aforementioned embodiments, such as a hard disk or memory. The computer-readable storage medium may also be an external storage device of any device with data processing capabilities, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store data that has been output or is to be output.

[0089] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the method for extracting a millimeter-wave radar range and azimuth map.

[0090] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of the present application are indicated by the claims.

[0091] It should be understood that the above general description and the detailed description that follows are exemplary and explanatory only and do not limit the present application. The present application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from the scope of the present application. The scope of the present application is limited only by the appended claims.

Claims

1. A method for extracting a millimeter-wave radar range-azimuth map, characterized in that: The method includes: S1. Traverse each distance unit in the millimeter wave radar to obtain a data matrix. The size of the data matrix is ​​obtained by the number of virtual receiving channels and the chirp sent by the radar in one frame. S2. Coherently accumulate the data matrix along the dimension of the virtual receiving channel, then perform Fourier transform on the chirp dimension and take the absolute value to obtain the Doppler spectrum absolute value sequence; S3, performing Doppler peak detection on the Doppler spectrum absolute value sequence to obtain the Doppler peak position; S4, converting the Doppler peak position into a Doppler phase shift, assigning weights according to the pks values ​​of different peaks and calculating the sum to obtain a Doppler peak phase compensation value, normalizing the Doppler peak phase compensation value and constructing a corresponding compensation matrix according to the size of the data matrix; S5. Compensate the data matrix according to the compensation matrix, calculate the data noise defense difference based on the compensated data matrix, traverse each angle to calculate the steering vector, calculate the response of the MVDR filter at each angle, and integrate the results into the millimeter wave radar range and azimuth map.

2. The method for extracting a millimeter-wave radar range-azimuth map according to claim 1, wherein: The extraction of the data matrix specifically includes: traversing each distance unit, extracting a data matrix X1 of size Nchannel*Nchirp from the radar data matrix that has undergone a distance dimension fast Fourier transform, where Nchannel represents the number of virtual receiving channels and Nchirp represents the number of chirps sent by the radar in one frame.

3. The method for extracting a millimeter-wave radar range-azimuth map according to claim 1, wherein: The specific steps in S2 are: X1_fft_abs=abs(fft(X1 array ,Nfft)) Where Nfft represents the number of Doppler fast Fourier transform points, X1 is the extracted data matrix, Nchannel represents the number of virtual receiving channels, Nchirp represents the number of chirps sent by the radar in one frame, and X1 array is the coherent accumulation result, and abs(.) means taking the absolute value.

4. The method for extracting a millimeter-wave radar range-azimuth map according to claim 1, wherein: The Doppler peak detection includes constant false alarm rate detection or findpeaks function detection. The constant false alarm rate detection is specifically as follows: detecting the target peak, recording all detected peaks and their peak positions idx, recorded as a set pks_location=[peakslocation], and the number of peaks is recorded as Npks; the findpeaks function detection is specifically as follows: after the findpeaks function is executed, the peaks and their positions are sorted from large to small according to the peak size, and the top N peaks and their positions are stored in the pks_location set matrix.

5. The method for extracting a millimeter-wave radar range-azimuth map according to claim 1, wherein: The conversion of the Doppler peak position into the Doppler phase shift is specifically: Among them, Npks is the number of Doppler peaks, Nfft is the number of Doppler fast Fourier transform points, location i is the position corresponding to the i-th peak, and NTx is in the denominator because the two values ​​before and after the chirp dimension in this X1 matrix are separated by NTx chirp cycles sent by the radar.

6. The method for extracting a millimeter-wave radar range-azimuth map according to claim 1, wherein: The Doppler peak phase compensation value obtained by allocating weights according to the pks values ​​of different peaks and calculating the sum is specifically: Among them, Npks is the number of Doppler peaks, pksphase i is the Doppler phase shift, and pks is the peak amplitude corresponding to each index value.

7. The method for extracting a millimeter-wave radar range-azimuth map according to claim 6, wherein: The construction of the compensation matrix includes: The Doppler compensation array is: Where NRx means the number of receiving antenna units, NTx*NRx is the number of receiving channels Nchannel, comp fai That is the phase value of the normalized Doppler-phase compensation value doppler_comp obtained; The Doppler compensation matrix obtained according to the Doppler compensation array is: The size of the obtained Doppler compensation matrix doppler_comp_matrix is ​​Nchannel*Nchirp, where Nchirp represents the number of chirps sent by the radar in one frame.

8. The method for extracting a millimeter-wave radar range-azimuth map according to claim 1, wherein: The compensation of the data matrix according to the compensation matrix is ​​specifically as follows: dot multiplication of the data matrix X1 and the compensation matrix doppler_comp_matrix to obtain the compensated data matrix X1_comp: X1_comp=X1·doppler_comp_matrix.

9. A device for extracting a millimeter-wave radar range-azimuth map, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, a method for extracting a millimeter-wave radar range-azimuth map according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, a method for extracting a millimeter-wave radar range-azimuth map according to any one of claims 1 to 8 is implemented.

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