Joint Multi-Beam Angle Radar High-Resolution Imaging Method Based on Least Squares Method

Abstract

The present invention discloses a joint multi-beam angle radar high-resolution imaging method based on the least squares method. By establishing a complete signal model, this method incorporates the sidelobe and grating lobe signals of the transmitted beam into the imaging process, avoiding treating them as interference but rather as an effective supplement to the reflected information of the target direction. The present invention proposes a joint estimation method based on the least squares method, which significantly reduces sidelobe artifacts, improves angular resolution, and alleviates the interference problem of grating lobes in sparse arrays by performing overall optimization processing on the echo signals of multiple transmitted beam angles. This method can effectively improve the imaging resolution of millimeter-wave radar in complex scenarios and has broad practical application potential.

Description

Technical Field

[0001] The present invention relates to the field of millimeter-wave radar, and particularly to a high-resolution imaging method for a joint multi-beam angle radar 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 fields such as autonomous driving, drone navigation, and security monitoring. As a key mode in radar imaging, the beamforming mode achieves 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 a sparse antenna array, 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 non-desired directions, while grating lobes can generate energy peaks in other directions, 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 through methods of joint transmit and receive beamforming, but these methods simply regard 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 dependence on uniform antenna arrangement and beamforming pattern optimization. Therefore, how to reduce sidelobe and grating lobe interference while fully utilizing the reflection information contained in sidelobes and grating lobes in the millimeter-wave radar beamforming mode has become a key issue. Summary of the Invention

[0004] The object of the present invention is to propose a high-resolution imaging method for a joint multi-beam angle radar based on the least squares method in view of the deficiencies of the prior art.

[0005] The object of the present invention is achieved by the following technical solutions: A high-resolution imaging method for a joint multi-beam angle radar based on the least squares method, comprising the following steps:

[0006] S1. Use a millimeter-wave frequency-modulated continuous-wave radar with multiple transmit and receive antennas, sample in the beamforming operating mode, and preprocess the sampled data 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 stationary targets and a three-dimensional tensor of moving targets according to whether the Doppler is zero;

[0008] S3. Perform distance slicing operations on the three-dimensional tensors of stationary targets and moving targets respectively to obtain beamforming angle-receive antenna slices corresponding to different distances Matrix, containing the gains in each direction of the beam;

[0009] S4. According to the beamforming angle - receiving antenna slice Matrix, using the least squares method to estimate the angle where the imaging target is located at this distance;

[0010] S4. Concatenate the calculation results of the least squares algorithm for different distance slices to obtain the initial distance - angle matrix, perform constant false alarm detection, and obtain the imaging results of static and moving targets.

[0011] Furthermore, the preprocessing of the sampled data specifically includes: performing distance point FFT and Doppler point FFT processing on the received data in sequence to obtain the preprocessed four - dimensional tensor, where is the number of samples within a chirp, is the number of repetitions of the same chirp at each angle, is the number of beamforming angles, is the number of receiving antennas.

[0012] Furthermore, the Doppler processing of the four - dimensional tensor is specifically: output one slice with Doppler being 0 as the static target distance - beamforming angle - receiving antenna three - dimensional tensor, and average multiple slices with non - zero Doppler in the Doppler dimension as the moving target distance - beamforming angle - receiving antenna three - dimensional tensor;

[0013] Furthermore, the operation of taking distance slices includes: for the beamforming angle - receiving antenna slices corresponding to different distances in the three - dimensional tensor matrix .

[0014] The th column of the slice matrix has the following expression:

[0015]

[0016] where represents the transmit beamforming matrix, and here represents the steering vector. For any angle , , represents the distance between the th transmit antenna and the th transmit antenna, represents the wavelength corresponding to the radar carrier frequency, Indicates the phase change difference of the signals transmitted by different antennas in each direction. Is the discretized direction. Indicates the phase change of the echo signal in each direction to the th receiving antenna. Indicates The distance between the th receiving antenna and the th receiving antenna. Indicates that when the distance is under the object scattering coefficients at the corresponding points of the discrete directions. Is the additive noise, following a complex Gaussian distribution with a mean of 0.

[0017] Furthermore, the specific process of estimating the angle where the imaging target is located at this distance using the least squares method specifically includes: using the constrained least squares method to solve the optimization problem to obtain the constrained least squares solution .

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

[0019] , and the parameter is 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 , setting the guard band length and the training band length , the false alarm rate , for each cell of the distance-angle matrix, respectively calculating the noise level , where the matrix has been supplemented with zeros, and the corresponding value is set to 0 when the subscript exceeds the maximum subscript of the original matrix;

[0021] Then, compare each cell value with the noise level. If it exceeds the threshold, retain the cell value; otherwise, set it to 0.

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

[0023] According to another aspect of the specification, there is also provided a joint multi-beam angle radar high-resolution imaging device based on the least squares method, including a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it implements the joint multi-beam angle radar high-resolution imaging method based on the least squares method described above.

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

[0025] Advantages of the present invention: The present invention integrates sidelobe and grating lobe signals, which are regarded as interference in traditional methods, into a part of the imaging information, and fully explores the target scattering characteristics contained therein. Through multi-angle joint processing, the gain in different directions of the beam is incorporated into the calculation process of the least squares method; by making full use of sidelobe and grating lobe information, sidelobe and grating lobe signals are regarded as useful data reflected by the target rather than interference, significantly improving the imaging resolution; applicable to sparse antenna arrays; overall optimizing the calculation, through multi-angle joint modeling, avoiding the limitations of angle-by-angle processing, improving the algorithm efficiency and reducing false target interference. Description of the Drawings

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

[0027] Figure 2 is the scene diagram of the algorithm simulation and the scattering points considered in ray tracing;

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

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

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

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

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

[0033] Figure 8 is a schematic diagram of the joint multi-beam angle radar high-resolution imaging device based on the least squares method provided in the embodiment of the present invention. Detailed Embodiments

[0034] To better understand the technical solution of the present application, the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0035] It should be clear that the described embodiments are only a part of the embodiments of this application, rather than all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

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

[0037] This embodiment provides a joint multi-beam angle radar high-resolution imaging method based on the least squares method. As Figure 1 shown, in the case of sparse transmitting and receiving antennas, there are significant beam gains in the sidelobe and grating lobe directions. 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 scattering intensity estimation in other directions. The traditional method filters out the reflected signals in other directions at each beam scanning direction, while in this embodiment, a joint multi-beam angle radar high-resolution imaging algorithm based on the least squares method is used to retain all received signals in all directions and uniformly use the least squares method to solve the scattering coefficients of scatterers in each direction after the radar scanning is completed, so as to make full use of the reflected signal information in the main lobe, sidelobe, and grating lobe directions of the radar and improve the final imaging resolution. The specific steps are as follows:

[0038] 1) Use a millimeter-wave frequency-modulated continuous-wave radar with multiple transmitting and receiving antennas and sample in the beamforming working mode. The received data is a four-dimensional tensor, and the sizes of the four dimensions are the number of samples within one chirp , the number of repetitions of the same chirp at each angle , the number of beamforming angles , and the number of receiving antennas . Perform distance point FFT and Doppler point FFT processing on the received data in sequence to obtain a preprocessed four-dimensional tensor;

[0039] 2) Perform Doppler processing on the preprocessed four-dimensional tensor data in the Doppler dimension. Specifically: output one slice with Doppler being 0 as a static target distance-beamforming angle-receiving antenna three-dimensional tensor, and average multiple slices with non-zero Doppler in the Doppler dimension as a moving target distance-beamforming angle-receiving antenna three-dimensional tensor;

[0040] 3) For the beamforming angle - receiving antenna three - dimensional tensor of the static target distance, for different distances corresponding to the beamforming angle - receiving antenna slices matrix , use the least - squares method to estimate the angle where the imaging target is located at this distance. The specific calculation method is as follows: In a radar system with transmitting receiving linear array antennas, and the transmitting and receiving antennas are parallel to each other. If the beamforming is swept through different directions , after the received signal undergoes the pre - processing in steps 1) and 2), and the distance slicing operation in step 3), the th column of the sliced matrix has the following expression:

[0041]

[0042] where represents the transmitting beamforming matrix, here represents the steering vector. For any angle , , represents the distance between the th transmitting antenna and the th transmitting antenna, represents the wavelength corresponding to the radar carrier frequency, represents the phase change difference of the signals transmitted by different antennas in each direction, is the discretized direction, represents the phase change of the echo signal in each direction to the th receiving antenna, represents the distance between the th receiving antenna and the represents the object scattering coefficient at the corresponding points of discrete directions when the distance is . is the additive noise, and it follows a complex Gaussian distribution with a mean of 0. Use the constrained least - squares method to solve the optimization problem , and obtain the constrained least - squares solution:

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

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

[0045] 5) For the three-dimensional tensor of moving target distance-beamforming angle-receiving antenna obtained in step 2), repeat the least squares method in steps 3) and 4), stitch the calculation results, and perform the constant false alarm detection process 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. One of them can be selected according to the subsequent actual application requirements, or both can be used.

[0047] Figure 2 is the plane scene diagram of computer simulation, which is three 1.5m×1.5m squares separated by 0.5m. The circles represent the direct path target points actually considered in the ray tracing. Using Figure 3 the 9-transmit 16-receive sparse antenna layout shown, the positions of the transmit and receive antennas are set to be the same as those of the TI AWR2243 radar. In this scenario, the side of the square facing the radar has a larger reflection surface and stronger reflected signals, while the side not facing the radar has a smaller radar cross-section and weaker reflected signals. Figure 4 and Figure 5 are the static target imaging results of the baseline scheme and the proposed scheme when the signal-to-noise ratio is 0dB, respectively. The proposed scheme of the present invention identifies more points on the side of the square. Figure 6 Compares the chamfer distances of the baseline scheme and the proposed scheme of the present invention. Here, the chamfer distance is a measure of the gap between the imaging result of the algorithm and the real target point cloud. The smaller the chamfer distance, the better the imaging effect. In Figure 6 , at each signal-to-noise ratio from -10dB to 10dB, the chamfer distance of the proposed scheme of the present invention is significantly lower than that of the baseline scheme. Figure 7Consider the problem of distinguishing two adjacent target points. The positions of two stationary target points are 5m at a direction of 20° and 5.03m at a direction of 23° respectively. The successful recognition probability of the solution of the present invention is higher than that of the baseline solution, and it can achieve a successful recognition probability close to 100% at a signal-to-noise ratio 20 dB lower than that of the baseline solution. This shows that the solution of the present invention has stronger ultimate resolution ability compared with the baseline solution.

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

[0049] See Figure 8 , an embodiment of a joint multi-beam angle radar high-resolution imaging device based on the least squares method provided by an embodiment of the present invention includes a memory and one or more processors. Executable code is stored in the memory. When the processor executes the executable code, it is used to implement a joint multi-beam angle radar high-resolution imaging method in the foregoing embodiment.

[0050] An embodiment of a joint multi-beam angle radar high-resolution imaging device provided by the present invention can be applied to any device with data processing capabilities. The any device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a logically meaningful device, it is formed by the processor of any device with data processing capabilities where it is located reading the corresponding computer program instructions in the non-volatile memory into the memory for operation. From the hardware level, as Figure 8 shown, it is a hardware structure diagram of any device with data processing capabilities where a joint multi-beam angle radar high-resolution imaging device provided by the present invention is located. Except for Figure 8 the shown processor, memory, network interface, and non-volatile memory, any device with data processing capabilities where the device in the embodiment is located usually further includes other hardware according to the actual functions of the any device with data processing capabilities, which will not be elaborated here.

[0051] The implementation processes of the functions and roles of each unit in the above device are specifically detailed in the implementation processes of the corresponding steps in the above method, which will not be elaborated here.

[0052] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial descriptions of the method embodiments. The device embodiments described above are merely illustrative. 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 to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present invention. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0053] An embodiment of the present invention further provides a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, it implements a method for joint multi-beam angle radar high-resolution imaging based on the least squares method in the above embodiments.

[0054] The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the foregoing 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. Further, 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 can also be used to temporarily store the data that has been output or will be output.

[0055] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the method for joint multi-beam angle radar high-resolution imaging based on the least squares method.

[0056] After considering the specification and practicing the content disclosed herein, those skilled in the art will easily think of other implementation schemes of the present application. The present application aims to cover any variations, uses or adaptive changes of the present application, and these variations, uses or adaptive changes follow the general principles of the present application and include the common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the claims.

[0057] It should be understood that the foregoing general description and the following detailed description are merely exemplary and explanatory, and do not limit this application. This application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is only limited by the appended 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; The distance slicing operation includes: The corresponding beamforming angle-receive antenna slice N×K matrix Slice the kth column of the matrix There are the following expressions: Y l,k =W H AD k F l +n l,k ,k=0,1,...,K-1, where W = [a(θ0)...a(θ N-1 )], represents the transmit beamforming matrix, is the steering vector, d i represents the distance between the i-th transmitting antenna and the 0-th transmitting antenna, λ represents the wavelength corresponding to the radar carrier frequency, A=[a(β0)...a(β P-1 )], represents the phase change difference of the signals transmitted by different antennas in different directions, is the discretization direction, represents the phase change of the echo signal in each direction to the kth receiving antenna, r k Indicates the distance between receiving antenna No. k and receiving antenna No. 0; Represents the object scattering coefficient at the corresponding points in P discrete directions at a distance of l; is additive noise, obeying a complex Gaussian distribution with a mean of 0; Where L is the number of sampling points in a chirp, N is the number of beamforming angles, and K is the number of receiving antennas. 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; S5. 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 L point FFT and Doppler M point FFT processing on the received data in sequence to obtain a preprocessed L×M×N×K four-dimensional tensor, where L is the number of sampling points in a chirp, M is the number of repetitions of the same chirp at each angle, N is the number of beamforming angles, and K 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 a three-dimensional tensor of static target distance-beamforming angle-receiving antenna L×N×K, and averaging multiple slices with Doppler non-0 in the Doppler dimension as a three-dimensional tensor of moving target distance-beamforming angle-receiving antenna L×N×K.

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 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 Obtain the constrained least squares solution 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 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: The parameter α is set to 0.1 times the estimated signal-to-noise ratio.

6. 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 constant false alarm detection specifically includes: according to the initial distance-angle matrix Set the guard band length L guard and training belt length L train , false alarm rate P fa , for each unit of the distance angle matrix Calculate the noise level separately 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.

7. The method for high-resolution imaging of joint multi-beam angular radar based on least square method according to claim 6, characterized in that: The exceeding threshold in the constant false alarm detection is specifically:

8. 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 7 is implemented.

9. 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 7 is implemented.

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