Millimeter wave radar MIMO multichannel signal sparse separation reconstruction method and system

Abstract

The invention discloses a millimeter wave radar MIMO multichannel signal sparse separation reconstruction method and system, belongs to the technical field of MIMO radar target detection, and solves the problem of how to improve the MIMO multichannel signal separation efficiency and accuracy, and the method comprises the steps: obtaining a millimeter wave radar echo signal, and converting the millimeter wave radar echo signal into a 4D matrix; performing fast Fourier transform on the 4D matrix to obtain a frequency domain 4D complex matrix and distance-Doppler spectrum data; further obtaining distance-Doppler unit index value pairs corresponding to all peak values; inputting the distance-Doppler unit index value pair into the frequency domain 4D complex matrix to obtain an MIMO multichannel signal two-dimensional matrix; based on the sparse number matrix corresponding to the virtual array, combining with the MIMO multichannel signal two-dimensional matrix, obtaining a two-dimensional sparse reconstruction matrix through numerical relation mapping, and performing vacancy position zero padding operation on the two-dimensional sparse reconstruction matrix to obtain a two-dimensional reconstruction matrix; according to the invention, the efficiency and accuracy of MIMO multi-channel signal separation are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of MIMO radar target detection and relates to a millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method and system. Background Art

[0002] Millimeter-wave radar refers to radar operating in the millimeter-wave frequency band (30 GHz to 300 GHz). Compared to other sensors like lidar and cameras, millimeter-wave radar offers superior spatial resolution, penetration, anti-interference capabilities, and all-weather operation, offering broad application prospects in cutting-edge low-altitude economic detection.

[0003] Multiple-input, multiple-output (MIMO) radar in time-division multiplexing (TDM) mode is a radar system that integrates multiple transmitters and multiple receivers. It is the most commonly used in engineering applications and research. MIMO technology can improve radar angle detection performance. However, to meet the diverse engineering application requirements, MIMO radar antenna arrays vary, and most are non-uniform. This poses challenges to the robustness and accuracy of two-dimensional angle estimation.

[0004] In existing technologies, MIMO radar two-dimensional angle estimation still has the following problems: 1) Channels are not effectively separated, and the non-uniform grid constrains fast beamforming technology, resulting in low accuracy when directly used for actual angle measurement, leading to large detection errors; 2) Existing super-resolution algorithms and deep learning algorithms are mostly limited by computing time and prior information, and their engineering application scenarios are restricted; 3) Some existing methods detect azimuth and pitch angles separately and achieve joint two-dimensional angle estimation through angle matching calculations, which lacks intuitiveness, and the angle matching calculations are cumbersome and consumes a lot of memory resources.

[0005] Therefore, a highly robust and low-complexity two-dimensional angle joint estimation method is necessary. How to improve the efficiency and accuracy of MIMO multi-channel signal separation, break through the inherent dependence of fast beamforming technology on uniform arrays, and thus improve the processing efficiency and robustness of diverse and complex radar data are problems that technical personnel in this field urgently need to solve. Summary of the Invention

[0006] The technical solution of the present invention is used to solve the problem of how to improve the efficiency and accuracy of MIMO multi-channel signal separation.

[0007] The present invention solves the above technical problems through the following technical solutions: The present invention provides a millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method, comprising the following steps: S1 obtains the echo signal of the millimeter-wave radar and converts it into multiple 4D matrices; S2 performs fast Fourier transform on the 4D matrix to obtain a frequency domain 4D complex matrix and range-Doppler spectrum data; S3 obtains the range-Doppler unit index value pairs corresponding to all peaks based on the range-Doppler spectrum data; S4 inputs all the range-Doppler unit index value pairs into the frequency domain 4D complex matrix to obtain a MIMO multi-channel signal 2D matrix; S5 is based on the sparse number matrix corresponding to the virtual array and the two-dimensional matrix of the MIMO multi-channel signal. A two-dimensional sparse reconstruction matrix is ​​obtained through numerical relationship mapping. The vacant positions of the two-dimensional sparse reconstruction matrix are filled with zeros to obtain a two-dimensional reconstruction matrix.

[0008] Furthermore, the 4D matrix acquisition method is: 1) Extracting a preset number of signed integer data as raw data based on the echo signal; 2) The first half of the original data is used as the real data, and the second half of the original data is used as the imaginary data; 3) Use the complex function to combine the real and imaginary data to generate complex data; 4) Sort and reorganize the complex data to obtain multiple 4D matrices. The specific method is: according to the number of data frames , Number of sweep signals per frame , frequency sampling points , Number of radar transmission channels and the number of radar receiving channels The obtained complex data is sorted and reorganized in the order of A 4D matrix [ ].

[0009] Furthermore, the 4D matrix of any frame is expressed as:

[0010] in, represents the slope of the FMCW wave emitted by the radar, Represents the imaginary unit, satisfying , represents the speed of light, Indicates the operating frequency, represents the sampling time, Indicates the sweep signal time, Indicates the radial distance of the reflection point relative to the radar center, represents the relative speed, It represents the angle between the reflection point and the vertical plane where the radar center is located. It represents the angle between the reflection point and the horizontal plane where the radar center is located. Indicates the radar antenna unit spacing.

[0011] Furthermore, for any frame of the 4D matrix Performing fast Fourier transform along the first and second dimensions, the range-Doppler data is obtained as follows:

[0012] in, Indicates the time of a Chirp signal, Indicates the time of one frame of Chirp signal, Indicates the distance frequency point, Indicates the Doppler frequency.

[0013] Furthermore, a method for obtaining the range-Doppler unit index value pair is as follows: a CA-CFAR algorithm is used to perform peak detection on the range-Doppler spectrum data, and the abscissa and ordinate of the peak are used as the range-Doppler unit index value pair.

[0014] Furthermore, the method for obtaining the sparse number matrix is ​​as follows: Millimeter wave radar has Root transmitting antenna and Root receiving antenna; Millimeter-wave radar operates in TDM-MIMO mode; The mth transmitting antenna in the millimeter wave radar transmits a signal with preset parameters; All receiving antennas of the millimeter-wave radar receive the echo signal simultaneously; Based on the geometric position of the millimeter wave radar receiving antenna unit, the corresponding virtual array units are sequentially arrive Number them to get a sparse number matrix.

[0015] Furthermore, the method for obtaining a two-dimensional sparse reconstruction matrix through numerical relationship mapping is as follows: One of the numbers in the sparse number matrix is The row index is: ; The column index is ; The coordinates in the two-dimensional matrix of the MIMO multi-channel signal are The element value of is directly mapped to the sparse numbered matrix numbered , thereby obtaining a two-dimensional sparse reconstruction matrix.

[0016] The present invention also provides a MIMO multi-channel signal sparse separation and reconstruction system for millimeter wave radar, comprising: a signal acquisition module, a signal processing module, an index value pair acquisition module, a MIMO multi-channel signal two-dimensional matrix acquisition module and a matrix sparse reconstruction module; The signal acquisition module is used to acquire the echo signal of the millimeter wave radar and convert it into multiple 4D matrices; The signal processing module is used to perform fast Fourier transform on the 4D matrix to obtain a frequency domain 4D complex matrix and range-Doppler spectrum data; The index value pair acquisition module is used to obtain the range-Doppler unit index value pairs corresponding to all peaks based on the range-Doppler spectrum data; The MIMO multi-channel signal two-dimensional matrix acquisition module is used to input all range-Doppler unit index value pairs into the frequency domain 4D complex matrix to obtain the MIMO multi-channel signal two-dimensional matrix; The matrix sparse reconstruction module is used to obtain a two-dimensional sparse reconstruction matrix through numerical relationship mapping based on the sparse number matrix corresponding to the virtual array and the two-dimensional matrix of the MIMO multi-channel signal, and perform a zero-filling operation on the vacant positions of the two-dimensional sparse reconstruction matrix to obtain a two-dimensional reconstructed matrix.

[0017] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned millimeter-wave radar MIMO multi-channel signal sparse separation and reconstruction method, and the processor is configured to execute the program stored in the memory.

[0018] The present invention also provides a storage medium having a computer program stored thereon. When the computer program is run by a processor, the steps of the above-mentioned millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method are executed.

[0019] The beneficial effects of the present invention are as follows: The present invention is based on the sparse numbered matrix corresponding to the virtual array, and realizes two-dimensional matrix reconstruction through numerical relationship mapping, thereby improving the accuracy of two-dimensional angle joint estimation and reducing computing time and computing resources; the present invention makes full use of the acquired complex-valued data, fully considers the geometric characteristics of the radar antenna array, improves the generalization ability of the MIMO signal channel separation program, and enables it to better adapt and understand when processing diverse and complex radar data, thereby improving the overall real-time performance and robustness; compared with the existing technology, the present invention effectively realizes MIMO signal channel separation in TDM mode, and considers the geometric structures of different radar antennas to solve the problems of MIMO signal channel separation and joint angular spectrum imaging based on two-dimensional array, and is convenient and universal; the present invention improves the efficiency and accuracy of MIMO multi-channel signal separation, thereby improving the processing efficiency and robustness of diverse and complex radar data, has high engineering practical value, and provides a reference idea and practical path for the signal processing development of various millimeter-wave radars. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of a millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method of the present invention; Figure 2 Schematic diagram of the structure of the echo signal acquisition device of the present invention; Figure 3 Schematic diagram of the 4D matrix generation process of the present invention; Figure 4 Schematic diagram of a stepped MIMO layout of the present invention; Figure 5 Schematic diagram of the MIMO multi-channel signal sparse separation and reconstruction process of the present invention; Figure 6 A schematic diagram showing the comparison between the results of two-dimensional angular spectrum imaging obtained by the method of the present invention and the case where multi-channel signal separation is not performed; Figure 7 Schematic diagram of the results of comparing the method of the present invention with the existing method; Figure 8 This is a structural diagram of the millimeter-wave radar MIMO multi-channel signal sparse separation and reconstruction system of the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] The technical solution of the present invention is further described below with reference to the accompanying drawings and specific embodiments: Example 1 like Figure 1 As shown, an embodiment of the present invention provides a millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method, including the following steps: Step 1: Obtain the echo signal of the millimeter-wave radar and convert it into multiple 4D matrices.

[0023] An echo signal acquisition device is used to acquire an echo signal, and the echo signal acquisition method is as follows: 1) Millimeter-wave radar operates in TDM-MIMO mode, with the transmitting antenna of the millimeter-wave radar transmitting electromagnetic wave signals in sequence; 2) The electromagnetic wave signal is reflected back by the detector as a sweep signal; 3) The frequency sweep signal is received by the receiving antenna of the millimeter wave radar to obtain an echo signal.

[0024] like Figure 2 As shown, the echo signal acquisition device includes: a PC device, a processor, a collector and a detector; Among them, the PC device is the computer user end, which is used to control the start of the entire data acquisition, analyze the radar receiving signal and realize data visualization; The processor is a data acquisition card and driver software, including a digital signal processor (DSP) and a field programmable gate array (FPGA). Through pre-cured programs, it transmits control signals to the back collector, sends waveform signals, and determines the signals emitted by the collector. The collector is a millimeter-wave radar, which includes an antenna array and a radio frequency front-end. It serves as a medium for sending and receiving electromagnetic wave signals. It is responsible for receiving control signals from the processor and sending electromagnetic wave signals to the detector. The detector is a non-metallic bracket equipped with a corner reflector, which is used to assume the target, reflect the received electromagnetic wave signal and generate an echo signal.

[0025] The echo signal acquisition device operates as follows: a PC is connected to a data collector, which is then connected to a processor. A control signal is sent from the PC to the data collector, initiating data acquisition. The processor transmits a pre-cured FMCW waveform signal to the data collector, which is the millimeter-wave radar. The millimeter-wave radar, operating in a time-determined multi-channel (TDM) mode, transmits electromagnetic wave signals sequentially according to the transmission channel. Part of the chirp signal is reflected by the detector. The reflected signal, which contains all the information about the corner reflector, is received by the millimeter-wave radar's receiving antenna and transmitted back to the PC, generating a data file, the echo signal.

[0026] like Figure 3As shown, the 4D matrix acquisition method is: 1) Extracting a preset number of signed integer data as raw data based on the echo signal; Extract valid 16-bit signed integer data as raw data based on the echo signal. The raw data length is filesize. 2) The first half of the original data is used as the real data, and the second half of the original data is used as the imaginary data; The first half of the original data is used as the real data I; the second half of the original data is used as the imaginary data Q; the lengths of the real data I and the imaginary data Q are both filesize / 2.

[0027] 3) Generate complex data based on the combination of real data and imaginary data; The real data I and the imaginary data Q are combined by the complex function to generate complex data, which contains more data information.

[0028] 4) Sort and reorganize the complex data to obtain multiple 4D matrices. The specific method is as follows: According to the number of data frames , Number of sweep signals per frame , frequency sampling points , Number of radar transmission channels and the number of radar receiving channels The obtained complex data is sorted and reorganized in the order of A 4D matrix [ ].

[0029] Then, the 4D matrix of any frame is expressed as:

[0030] in, represents the slope of the FMCW wave emitted by the radar, Represents the imaginary unit, satisfying , represents the speed of light, Indicates the operating frequency, represents the sampling time, Indicates the sweep signal time, Indicates the radial distance of the reflection point relative to the radar center, represents the relative speed, It represents the angle between the reflection point and the vertical plane where the radar center is located. It represents the angle between the reflection point and the horizontal plane where the radar center is located. Indicates the radar antenna unit spacing.

[0031] Step 2: Perform fast Fourier transform on the 4D matrix to obtain a frequency domain 4D complex matrix and range-Doppler spectrum data.

[0032] According to the errors in the millimeter wave radar itself in terms of manufacturing process, signal propagation and environmental factors, this embodiment will use the pre-saved calibration data to generate the above A 4D matrix [ ] (time domain) calibration to improve the accuracy of angle detection. Each millimeter-wave radar only needs one set of calibration data.

[0033] The 4D matrix includes four dimensions, where the first dimension represents the distance dimension, the second dimension represents the Doppler dimension, the third dimension represents the transmission channel dimension, and the fourth dimension represents the receiving channel dimension.

[0034] Based on the calibrated 4D matrix, a fast Fourier transform is performed along the range and Doppler dimensions to obtain a frequency domain 4D complex matrix and range-Doppler spectrum data. The 4D matrix after fast Fourier transform processing is a frequency domain 4D complex matrix.

[0035] For any frame of 4D matrix Performing fast Fourier transform along the first and second dimensions, the range-Doppler data is obtained as follows:

[0036] in, Indicates the time of a Chirp signal, Indicates the time of one frame of Chirp signal, Indicates the distance frequency point, Indicates the Doppler frequency.

[0037] Step 3: Obtain the range-Doppler unit index value pairs corresponding to all peaks based on the range-Doppler spectrum data.

[0038] Coherent accumulation is performed on the range-Doppler data to enhance the signal-to-noise ratio. The CA-CFAR (Cell-Averaging Constant False Alarm Rate) algorithm is used to detect peaks in the range-Doppler spectrum data, and the abscissa and ordinate of the peak are used as the range-Doppler cell index value pair.

[0039] Step 4: Input all the range-Doppler unit index value pairs into the frequency domain 4D complex matrix to obtain a MIMO multi-channel signal two-dimensional matrix.

[0040] All the range-Doppler unit index value pairs are input into the frequency domain 4D complex matrix, and each range-Doppler unit index value pair can obtain a MIMO signal channel data 2D matrix , including all, Virtual channel data.

[0041] Step 5: Based on the sparse number matrix corresponding to the virtual array and the two-dimensional matrix of the MIMO multi-channel signal, a two-dimensional sparse reconstruction matrix is ​​obtained through numerical relationship mapping, and a zero-filling operation is performed on the vacant positions of the two-dimensional sparse reconstruction matrix to obtain a two-dimensional reconstruction matrix.

[0042] The method for obtaining the sparse number matrix is ​​as follows: like Figure 4 As shown, millimeter wave radar has root( ) transmitting antenna and root( ) Receiving antenna; Millimeter-wave radar operates in TDM-MIMO mode; The mth transmitting antenna in the millimeter-wave radar transmits a signal with preset parameters (m=1, 2, 3); All receiving antennas of the millimeter-wave radar receive the echo signal simultaneously; Based on the geometric position of the millimeter wave radar receiving antenna unit, the corresponding virtual array units are sequentially arrive Number them to get a sparse number matrix.

[0043] like Figure 5 As shown, the method for obtaining a two-dimensional sparse reconstruction matrix through numerical relationship mapping is as follows: One of the numbers in the sparse number matrix is The row index is: ; column index is ; The coordinates of the MIMO multi-channel signal two-dimensional matrix are The element value of is directly mapped to the sparse numbered matrix numbered , thereby obtaining a two-dimensional sparse reconstruction matrix.

[0044] Experimental verification The two-dimensional reconstruction matrix is ​​subjected to two-dimensional beamforming. In this embodiment, the fast Fourier beamforming algorithm is directly used to obtain two-dimensional angular spectrum data. Spectral imaging is performed based on the amplitude of the two-dimensional angular spectrum data to obtain the position of the detector in the vertical plane. Figure 6 As shown in the figure, two-dimensional angular spectrum imaging is performed based on the obtained two-dimensional reconstruction matrix and compared with the case without multi-channel signal separation. The two bright spots show the positions of the two corner reflectors in the vertical plane. From the comparison results, it can be seen that the detection results after multi-channel signal separation are more accurate and more in line with the actual target position. Figure 7The figure shows a comparison between the proposed method and existing methods, showing the time required for a single calculation of different separation and reconstruction methods for 119 frames of data. From the comparison results, it can be seen that the proposed method takes less time for a single calculation and is more stable, with minimal time glitches.

[0045] Example 2 like Figure 8 As shown, a millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction system includes: a signal acquisition module, a signal processing module, an index value pair acquisition module, a MIMO multi-channel signal two-dimensional matrix acquisition module and a matrix sparse reconstruction module; The signal acquisition module is used to obtain the echo signal of the millimeter wave radar and convert it into multiple 4D matrices; The signal processing module is used to perform fast Fourier transform on the 4D matrix to obtain a frequency domain 4D complex matrix and range-Doppler spectrum data; An index value pair acquisition module is used to obtain the range-Doppler unit index value pairs corresponding to all peaks based on the range-Doppler spectrum data; A MIMO multi-channel signal two-dimensional matrix acquisition module is used to input all range-Doppler unit index value pairs into a frequency domain 4D complex matrix to obtain a MIMO multi-channel signal two-dimensional matrix; The matrix sparse reconstruction module is used to obtain a two-dimensional sparse reconstruction matrix by mapping the sparse number matrix corresponding to the virtual array and the two-dimensional matrix of the MIMO multi-channel signal, and perform a zero-filling operation on the vacant positions of the two-dimensional sparse reconstruction matrix to obtain a two-dimensional reconstruction matrix.

[0046] Example 3 An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method in Example 1, and the processor is configured to execute the program stored in the memory.

[0047] Example 4 A storage medium stores a computer program, which, when executed by a processor, executes the steps of the millimeter-wave radar MIMO multi-channel signal sparse separation and reconstruction method in embodiment 1.

[0048] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method, characterized in that: The following steps are involved: S1 obtains the echo signal of the millimeter-wave radar and converts it into multiple 4D matrices; S2 performs fast Fourier transform on the 4D matrix to obtain a frequency domain 4D complex matrix and range-Doppler spectrum data; S3 obtains the range-Doppler unit index value pairs corresponding to all peaks based on the range-Doppler spectrum data; S4 inputs all the range-Doppler unit index value pairs into the frequency domain 4D complex matrix to obtain a MIMO multi-channel signal 2D matrix; S5 is based on the sparse number matrix corresponding to the virtual array and the two-dimensional matrix of the MIMO multi-channel signal. A two-dimensional sparse reconstruction matrix is ​​obtained through numerical relationship mapping. The vacant positions of the two-dimensional sparse reconstruction matrix are filled with zeros to obtain a two-dimensional reconstruction matrix.

2. The millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method according to claim 1, characterized in that: The 4D matrix acquisition method is: 1) Extracting a preset number of signed integer data as raw data based on the echo signal; 2) The first half of the original data is used as the real data, and the second half of the original data is used as the imaginary data; 3) Use the complex function to combine the real and imaginary data to generate complex data; 4) Sort and reorganize the complex data to obtain multiple 4D matrices. The specific method is: according to the number of data frames , Number of sweep signals per frame , frequency sampling points , Number of radar transmission channels and the number of radar receiving channels The obtained complex data is sorted and reorganized in the order of A 4D matrix [ ].

3. The millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method according to claim 2, characterized in that: The 4D matrix of any frame is represented as: in, represents the slope of the FMCW wave emitted by the radar, Represents the imaginary unit, satisfying , represents the speed of light, Indicates the operating frequency, represents the sampling time, Indicates the sweep signal time, Indicates the radial distance of the reflection point relative to the radar center, represents the relative speed, It represents the angle between the reflection point and the vertical plane where the radar center is located. It represents the angle between the reflection point and the horizontal plane where the radar center is located. Indicates the radar antenna unit spacing.

4. The millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method according to claim 3 is characterized in that: For any frame of 4D matrix Performing fast Fourier transform along the first and second dimensions, the range-Doppler data is obtained as follows: in, Indicates the time of a Chirp signal, Indicates the time of one frame of Chirp signal, Indicates the distance frequency point, Indicates the Doppler frequency.

5. The millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method according to claim 4, characterized in that: The method for obtaining the range-Doppler unit index value pair is as follows: the CA-CFAR algorithm is used to perform peak detection on the range-Doppler spectrum data, and the abscissa and ordinate of the peak are used as the range-Doppler unit index value pair.

6. The millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method according to claim 5, characterized in that: The method for obtaining the sparse number matrix is ​​as follows: Millimeter wave radar has Root transmitting antenna and Root receiving antenna; Millimeter-wave radar operates in TDM-MIMO mode; The mth transmitting antenna in the millimeter wave radar transmits a signal with preset parameters; All receiving antennas of the millimeter-wave radar receive the echo signal simultaneously; Based on the geometric position of the millimeter wave radar receiving antenna unit, the corresponding virtual array units are sequentially arrive Number them to get a sparse number matrix.

7. The millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method according to claim 6, characterized in that: The method for obtaining a two-dimensional sparse reconstruction matrix by numerical relationship mapping is as follows: One of the numbers in the sparse number matrix is The row index is: ; The column index is ; The coordinates in the two-dimensional matrix of the MIMO multi-channel signal are The element value of is directly mapped to the sparse numbered matrix numbered , thereby obtaining a two-dimensional sparse reconstruction matrix.

8. A millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction system, characterized by: include: Signal acquisition module, signal processing module, index value pair acquisition module, MIMO multi-channel signal two-dimensional matrix acquisition module and matrix sparse reconstruction module; The signal acquisition module is used to acquire the echo signal of the millimeter wave radar and convert it into multiple 4D matrices; The signal processing module is used to perform fast Fourier transform on the 4D matrix to obtain a frequency domain 4D complex matrix and range-Doppler spectrum data; The index value pair acquisition module is used to obtain the range-Doppler unit index value pairs corresponding to all peaks based on the range-Doppler spectrum data; The MIMO multi-channel signal two-dimensional matrix acquisition module is used to input all range-Doppler unit index value pairs into the frequency domain 4D complex matrix to obtain the MIMO multi-channel signal two-dimensional matrix; The matrix sparse reconstruction module is used to obtain a two-dimensional sparse reconstruction matrix through numerical relationship mapping based on the sparse number matrix corresponding to the virtual array and the two-dimensional matrix of the MIMO multi-channel signal, and perform a zero-filling operation on the vacant positions of the two-dimensional sparse reconstruction matrix to obtain a two-dimensional reconstructed matrix.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the millimeter-wave radar MIMO multi-channel signal sparse separation and reconstruction method as described in any one of claims 1 to 7, and the processor is configured to execute the program stored in the memory.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the millimeter wave radar MIMO multi-channel signal sparse separation and reconstruction method according to any one of claims 1 to 7 are executed.

Images