Joint multi-beam angle radar high-resolution imaging method based on least square method

Abstract

The invention discloses a joint multi-beam angle radar high-resolution imaging method based on a least square method. According to the method, a complete signal model is established, side lobe and grating lobe signals of a transmitted beam are included in an imaging process, and the side lobe and grating lobe signals are prevented from being regarded as interference and are effectively supplemented to reflection information in a target direction. According to the joint estimation method based on the least square method, echo signals of a plurality of transmitting beam angles are subjected to overall optimization processing, so that sidelobe artifacts are remarkably reduced, the angle resolution is improved, and the interference problem of grating lobes in a sparse array is relieved. The method can effectively improve the imaging resolution of the millimeter wave radar in a complex scene, and has wide practical application potential.

Description

Technical Field

[0001] The invention relates to the field of millimeter wave radar, and in particular to a joint multi-beam angular radar high-resolution imaging method based on the least squares method. Background Art

[0002] Due to its frequency band characteristics, millimeter wave radar can achieve long-distance imaging under complex weather conditions, and has important applications in autonomous driving, drone navigation, security monitoring and other fields. As a key mode in radar imaging, beamforming mode achieves a high signal-to-noise ratio and spatial resolution by concentrating signal energy in a specific direction. However, traditional beamforming methods face significant challenges when dealing with sparse antenna arrays.

[0003] In sparse antenna arrays, although the increase in element spacing increases the equivalent aperture, it also introduces significant sidelobe and grating lobe effects. Sidelobes can cause interference signals in undesired directions, while grating lobes can produce energy peaks in other directions that are comparable to the main lobe intensity. These effects reduce the accuracy of target detection and limit the improvement of imaging resolution. Existing technologies usually attempt to suppress sidelobes and grating lobes by combining transmit and receive beamforming methods, but these methods simply treat sidelobe and grating lobe signals as interference or noise, ignoring the useful reflection information they contain. This processing method not only loses valuable target information, but also increases the reliance on uniform antenna arrangement and optimization of beamforming patterns. Therefore, how to fully utilize the reflection information contained in sidelobes and grating lobes while reducing sidelobe and grating lobe interference in millimeter-wave radar beamforming mode has become a key issue. Summary of the invention

[0004] The present invention aims to address the deficiencies of the prior art and to propose a joint multi-beam angular radar high-resolution imaging method based on the least squares method.

[0005] The object of the present invention is achieved through the following technical scheme: a joint multi-beam angular radar high-resolution imaging method based on least squares method, comprising the following steps:

[0006] S1. Using a millimeter-wave linear frequency modulation continuous wave radar with multiple transmitting and receiving antennas, sampling is performed in a beamforming working mode, and the sampled data is preprocessed to obtain a preprocessed four-dimensional tensor;

[0007] S2, perform Doppler processing on the four-dimensional tensor, and divide the output into a three-dimensional tensor of a static target and a three-dimensional tensor of a moving target according to whether the Doppler is zero;

[0008] S3. Perform distance slicing operations on the three-dimensional tensors of static targets and moving targets respectively to obtain beamforming angles corresponding to different distances - receiving antenna slices Matrix, containing the beam gains in all directions;

[0009] S4. Based on beamforming angle - receiving antenna slice Matrix, use the least squares method to estimate the angle of the imaging target at this distance;

[0010] S4. The calculation results of the least squares algorithm of different distance slices are spliced ​​to obtain an initial distance-angle matrix, and constant false alarm detection is performed to obtain imaging results of static targets and moving targets.

[0011] Furthermore, the preprocessing of the sampled data specifically includes: performing distance Point FFT, Doppler Point FFT processing, get the pre-processed A four-dimensional tensor, where is the number of sampling points in a chirp, is the number of repetitions of the same chirp at each angle, is the beamforming angle, is the number of receiving antennas.

[0012] Furthermore, the Doppler processing of the four-dimensional tensor is specifically: outputting a slice with Doppler of 0 as the static target distance-beamforming angle-receiving antenna Three-dimensional tensor, averaging multiple slices with non-zero Doppler in the Doppler dimension as moving target distance-beamforming angle-receiving antenna 3D tensor;

[0013] Furthermore, the distance slicing operation includes: performing a distance slicing operation on different distances in the three-dimensional tensor. Corresponding beamforming angles - receive antenna slices matrix .

[0014] Slice the matrix List There are the following expressions:

[0015]

[0016] in represents the transmit beamforming matrix, where Represents the steering vector, for any angle , , Indicates The transmitting antenna The distance from the transmitting antenna, represents the wavelength corresponding to the radar carrier frequency, Indicates the phase change difference of signals transmitted by different antennas in different directions. is the discretization direction, Indicates the echo signal in each direction to the The phase change of the receiving antenna, express The receiving antenna and The distance from the receiving antenna; Indicates when the distance Down The object scattering coefficient at the corresponding point in each discrete direction; is additive noise and follows a complex Gaussian distribution with a mean of 0.

[0017] Furthermore, the method of using the least squares method to estimate the angle of the imaging target at the distance specifically includes: using the constrained least squares method to solve the optimization problem , and obtain the constrained least squares solution .

[0018] Furthermore, the regularization function of the constrained least squares solution in the calculation process is: the 2-norm of the estimated scattering coefficients in different directions, and the specific formula is as follows:

[0019] ,parameter Set to 0.1 times the estimated signal-to-noise ratio.

[0020] Furthermore, the constant false alarm detection specifically includes: according to the initial distance-angle matrix , set the guard band length and training belt length , false alarm rate , for each unit of the distance angle matrix , respectively calculate the noise level , where the matrix has been padded with zeros, and when the subscript exceeds the maximum subscript of the original matrix, the corresponding value is set to 0;

[0021] Then, each cell value is compared with the noise level, and if it exceeds the threshold, the cell value is retained, otherwise it is set to 0.

[0022] Furthermore, the exceeding threshold in the constant false alarm detection is specifically: .

[0023] According to another aspect of the specification, a joint multi-beam angular radar high-resolution imaging device based on the least squares method is also provided, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, the joint multi-beam angular radar high-resolution imaging method based on the least squares method is implemented.

[0024] According to another aspect of the specification, a computer-readable storage medium is provided, on which a program is stored. When the program is executed by a processor, the least squares-based joint multi-beam angular radar high-resolution imaging method is implemented.

[0025] Beneficial effects of the present invention: The present invention integrates the sidelobe and grating lobe signals that are considered as interference by traditional methods into part of the imaging information, and fully exploits the target scattering characteristics contained therein. Through multi-angle joint processing, the gain of the beam in different directions is included in the calculation process of the least squares method; the sidelobe and grating lobe information is fully utilized, and the sidelobe and grating lobe signals are regarded as useful data reflected by the target rather than interference, which significantly improves the imaging resolution; it is suitable for sparse antenna arrays; the overall optimization calculation avoids the limitations of angle-by-angle processing through multi-angle joint modeling, improves the algorithm efficiency and reduces pseudo-target interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the beam scanning process under a sparse antenna array;

[0027] Figure 2 It is the scene graph simulated by the algorithm and the scattering points considered by ray tracing;

[0028] Figure 3 It is the layout of radar transceiver antennas;

[0029] Figure 4 is the imaging result of the baseline protocol;

[0030] Figure 5 is the imaging result of the present invention;

[0031] Figure 6 It is the relationship between the chamfer distance between the imaging result and the true value of the target position and the signal-to-noise ratio of the received signal when the joint multi-beam angle radar high-resolution imaging method based on the least squares method proposed in the present invention is compared with the baseline method;

[0032] Figure 7 The relationship between the successful recognition rate of two targets close in distance and angle and the signal-to-noise ratio of the received signal when comparing the joint multi-beam angle radar high-resolution imaging method based on the least squares method proposed in the present invention with the baseline method;

[0033] Figure 8 It is a schematic diagram of a joint multi-beam angular radar high-resolution imaging device based on the least squares method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0035] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0036] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0037] This embodiment provides a joint multi-beam angular radar high-resolution imaging method based on the least squares method. Figure 1 As shown in the figure, in the case of sparse transmitting and receiving antennas, the side lobe and grating lobe directions have significant beam gain. These reflected signals contain the scattering intensity information of objects in specific directions. Although this information has no effect on the signal estimation in the current beam direction, it is beneficial to the estimation of scattering intensity in other directions. The traditional method filters out the reflected signals in other directions in each beam scanning direction. In this example, a joint multi-beam angle radar high-resolution imaging algorithm based on the least squares method is used to retain the received signals in all directions, and after the radar scan is completed, the least squares method is uniformly used to solve the scattering coefficients of scatterers in each direction, thereby making full use of the reflected signal information in the radar main lobe, side lobe, and grating lobe directions to improve the final imaging resolution. The specific steps include:

[0038] 1) Use a millimeter-wave linear frequency modulation continuous wave radar with multiple transmitting and receiving antennas to perform sampling in beamforming mode. The received data is a four-dimensional tensor, and the sizes of the four dimensions are the number of sampling points in one chirp. , the number of repetitions of the same chirp at each angle , beamforming angle , the number of receiving antennas . The received data is then distance Point FFT, Doppler Point FFT processing, get the pre-processed 4D tensor;

[0039] 2) The pre-processed four-dimensional tensor data is Doppler processed in the Doppler dimension, specifically: a slice with Doppler of 0 is output as the static target distance-beamforming angle-receiving antenna Three-dimensional tensor, averaging multiple slices with non-zero Doppler in the Doppler dimension as moving target distance-beamforming angle-receiving antenna 3D tensor;

[0040] 3) Different distances in the three-dimensional tensor of static target distance-beamforming angle-receiving antenna Corresponding beamforming angles - receive antenna slices matrix , use the least squares method to estimate the angle of the imaging target at this distance. The specific calculation method is: hair In a radar system with linear array antennas and parallel transmitting and receiving antennas, if beamforming is used to scan Different directions After the received signal is preprocessed in steps 1) and 2) and the distance slice operation in step 3), the slice matrix List There are the following expressions:

[0041]

[0042] in represents the transmit beamforming matrix, where Represents the steering vector, for any angle , , Indicates The transmitting antenna The distance from the transmitting antenna, represents the wavelength corresponding to the radar carrier frequency, Indicates the phase change difference of signals transmitted by different antennas in different directions. is the discretization direction, Indicates the echo signal in each direction to the The phase change of the receiving antenna, express The receiving antenna and The distance from the receiving antenna. Indicates when the distance Down The scattering coefficient of the object at the corresponding point in each discrete direction. is additive noise and follows a complex Gaussian distribution with a mean of 0. Use constrained least squares to solve the optimization problem , and obtain the constrained least squares solution:

[0043] , where the regularization term It uses the prior information that the scattering points are not densely distributed in space and have low total energy. is the regularization parameter, The larger it is, the more concentrated the distribution of scatter points is. The general value range is 0.01 to 0.2 times the signal-to-noise ratio. The signal-to-noise ratio here can be replaced by an estimated signal-to-noise ratio. Set to 0.1 times the estimated signal-to-noise ratio.

[0044] 4) Concatenate the calculation results of the least squares algorithm for different distance slices in step 3) to obtain the initial distance-angle matrix , perform constant false alarm detection: set the protection band length and training belt length , false alarm rate , for each unit of the distance angle matrix , respectively calculate the noise level , where the matrix has been padded with zeros, and the corresponding value is set to 0 when the subscript exceeds the maximum subscript of the original matrix. Then, each cell value is compared with the noise level. If it exceeds the threshold, the cell value is retained, otherwise it is set to 0. Exceeding the threshold specifically means: .

[0045] 5) For the moving target distance-beamforming angle-receiving antenna three-dimensional tensor obtained in step 2), repeat the least squares method in steps 3) and 4), concatenate the calculation results, and perform constant false alarm detection to obtain the moving target imaging result.

[0046] 6) The static target imaging result obtained in step 4) and the moving target imaging result obtained in step 5) are the final imaging results. You can choose one of them or use both according to the subsequent actual application requirements.

[0047] Figure 2 It is a computer-simulated planar scene graph, which consists of three 1.5m×1.5m squares 0.5m apart. The circles represent the direct-path target points actually considered in ray tracing. Figure 3 The 9-transmitter and 16-receiver sparse antenna layout shown in the figure has the same position of the transmit and receive antennas as the TI AWR2243 radar. In this scenario, the side reflection surface facing the radar is larger and the reflected signal is stronger, while the side radar cross-section area not facing the radar is smaller and the reflected signal is weaker. Figure 4 and Figure 5 They are respectively the static target imaging results of the baseline scheme and the scheme of the present invention when the signal-to-noise ratio is 0 dB. The scheme of the present invention identifies more points on the side of the square. Figure 6 The chamfer distances of the baseline solution and the solution of the present invention are compared. The chamfer distance here measures the difference between the algorithm imaging result and the real target point cloud. The smaller the chamfer distance, the better the imaging effect. Figure 6 In the figure, at each signal-to-noise ratio from -10dB to 10dB, the chamfer distance of the solution of the present invention is significantly lower than the chamfer distance of the baseline solution. Figure 7Considering the problem of distinguishing two adjacent target points, the two stationary target points are located at 5m, 20° direction, and 5.03m, 23° direction respectively. The success recognition probability of the scheme of the present invention is higher than that of the baseline scheme, and can reach a success recognition probability of nearly 100% when the signal-to-noise ratio is 20dB lower than that of the baseline scheme, which shows that the scheme of the present invention has stronger extreme resolution ability than the baseline scheme.

[0048] Corresponding to the aforementioned embodiment of a joint multi-beam angular radar high-resolution imaging method based on the least squares method, the present invention also provides an embodiment of a joint multi-beam angular radar high-resolution imaging device based on the least squares method.

[0049] See also Figure 8 An embodiment of the present invention provides a joint multi-beam angular radar high-resolution imaging device based on the least squares method, including a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, it is used to implement a joint multi-beam angular radar high-resolution imaging method based on the least squares method in the above embodiment.

[0050] An embodiment of a joint multi-beam angle radar high-resolution imaging device based on the least squares method 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 internal memory for execution. From a hardware perspective, if Figure 8 As shown, it is a hardware structure diagram of any device with data processing capability where a joint multi-beam angle radar high-resolution imaging device based on the least square method provided by the present invention is located, except Figure 8 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiments 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.

[0051] 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.

[0052] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0053] 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, a joint multi-beam angular radar high-resolution imaging method based on the least squares method in the above embodiment is implemented.

[0054] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device of any device with data processing capability, 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 capability. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0055] The present invention also provides a computer program product, comprising a computer program, and when the computer program is executed by a processor, the least squares-based joint multi-beam angular radar high-resolution imaging method is implemented.

[0056] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0057] It should be understood that the above general description and the detailed description below are only exemplary and explanatory and cannot limit the present application. The present application is not limited to the precise structure described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the attached claims.

Claims

1. A joint multi-beam angular radar high-resolution imaging method based on least squares method, characterized in that: The steps include: S1. Using a millimeter-wave linear frequency modulation continuous wave radar with multiple transmitting and receiving antennas, sampling is performed in a beamforming working mode, and the sampled data is preprocessed to obtain a preprocessed four-dimensional tensor; S2, perform Doppler processing on the four-dimensional tensor, and divide the output into a three-dimensional tensor of a static target and a three-dimensional tensor of a moving target according to whether the Doppler is zero; S3, respectively performing distance slicing operations on the three-dimensional tensors of the static target and the moving target to obtain beamforming angle-receiving antenna slice matrices corresponding to different distances, including beam gains in various directions; S4, using the least squares method to estimate the angle of the imaging target at the distance according to the beamforming angle-receiving antenna slice matrix; S4. The calculation results of the least squares algorithm of different distance slices are spliced ​​to obtain an initial distance-angle matrix, and constant false alarm detection is performed to obtain imaging results of static targets and moving targets.

2. The method for high-resolution imaging of joint multi-beam angular radar based on least squares method according to claim 1, characterized in that: The preprocessing of the sampled data specifically includes: performing distance Point FFT, Doppler Point FFT processing, get the pre-processed A four-dimensional tensor, where is the number of sampling points in a chirp, is the number of repetitions of the same chirp at each angle, is the beamforming angle, is the number of receiving antennas.

3. The method for high-resolution imaging of joint multi-beam angular radar based on least squares method according to claim 1, characterized in that: The Doppler processing of the four-dimensional tensor is specifically as follows: outputting a slice with Doppler of 0 as the static target distance-beamforming angle-receiving antenna Three-dimensional tensor, averaging multiple slices with non-zero Doppler in the Doppler dimension as moving target distance-beamforming angle-receiving antenna A three-dimensional tensor.

4. The method for high-resolution imaging of joint multi-beam angular radar based on least squares method according to claim 1, characterized in that: The distance slicing operation includes: Corresponding beamforming angles - receive antenna slices matrix , Slice the matrix List There are the following expressions: ， in represents the transmit beamforming matrix, where Represents the steering vector, for any angle , , is the number of transmitting antennas, Indicates The transmitting antenna The distance from the transmitting antenna, represents the wavelength corresponding to the radar carrier frequency, Indicates the phase change difference of signals transmitted by different antennas in different directions. is the discretization direction, , indicating the echo signal in each direction to the The phase change of the receiving antenna, express The receiving antenna The distance to the receiving antenna; Indicates when the distance Down The object scattering coefficient at the corresponding point in each discrete direction; is additive noise and follows a complex Gaussian distribution with a mean of 0.

5. The method for high-resolution imaging of joint multi-beam angular radar based on least square method according to claim 4, characterized in that: The method of using the least squares method to estimate the angle of the imaging target at the distance specifically includes: using the constrained least squares method to solve the optimization problem , and obtain the constrained least squares solution .

6. The method for high-resolution imaging of joint multi-beam angular radar based on least square method according to claim 5, characterized in that: The regularization function of the constrained least squares solution in the calculation process is: the 2-norm of the estimated scattering coefficients in different directions. The specific formula is as follows: ,parameter Set to 0.1 times the estimated signal-to-noise ratio.

7. The method for high-resolution imaging of joint multi-beam angular radar based on least square method according to claim 1, characterized in that: The constant false alarm detection specifically includes: according to the initial distance-angle matrix , set the guard band length and training belt length , false alarm rate , for each unit of the distance angle matrix , respectively calculate the noise level , where the matrix has been padded with zeros, and when the subscript exceeds the maximum subscript of the original matrix, the corresponding value is set to 0; Then, each cell value is compared with the noise level, and if it exceeds the threshold, the cell value is retained, otherwise it is set to 0.

8. The method for high-resolution imaging of joint multi-beam angular radar based on least square method according to claim 7, characterized in that: The exceeding threshold in the constant false alarm detection is specifically: .

9. A joint multi-beam angular radar high-resolution imaging device based on least squares method, 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 joint multi-beam angular radar high-resolution imaging method based on least squares method as described in 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 joint multi-beam angular radar high-resolution imaging method based on least squares method as described in any one of claims 1 to 8 is implemented.

Images