Method for multi-channel radar forward-looking imaging based on sparsity and weighted low rank

Abstract

The application discloses a kind of multi-channel radar forward-looking imaging methods based on sparse and weighted low rank, solve the problem that forward-looking super-resolution imaging effect is limited in prior art under complex imaging scene;The method comprises: establishing echo signal model under the mode of multi-channel radar single launch single receiving, and obtaining the echo signal corresponding to each target;Again, after pulse compression and correction processing, it is expressed as two-dimensional echo matrix, the target signal matrix in two-dimensional echo matrix is weighted, and the weighted target signal matrix with low rank characteristics is obtained;According to the sparsity of target in echo signal model and the low rank characteristics of weighted target signal matrix, the forward-looking imaging model is obtained;The forward-looking imaging model is solved based on ALM under the ADMM framework, and the reconstructed forward-looking high-resolution image is obtained;The method realizes through sparse and weighted low rank double constraint, and then obtains high-quality, high-resolution reconstructed forward-looking high-resolution image.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar imaging, and particularly relates to a multi-channel radar forward-looking imaging method based on sparsity and weighted low rank. BACKGROUND

[0002] Radar forward-looking imaging can provide fine electromagnetic scattering characteristics of targets in the front of a moving platform, and has various applications in military and civilian fields, including terrain mapping, autonomous driving, precision guidance, etc., and has become a hotspot and difficulty in current radar imaging technology research. In recent years, small moving platforms such as unmanned aerial vehicles and missiles have carried multi-channel radar high-resolution imaging systems, which has become one of the important directions of the development of modern radar imaging modes. Compared with traditional single-channel radars, multi-channel radars have higher spatial degrees of freedom, and can perform more active and autonomous information acquisition due to the ability to autonomously emit electromagnetic waves. By adjusting the parameters such as carrier frequency and waveform of the signal, the multi-channel radar can significantly improve the imaging performance of the system. In addition, considering that the target of interest usually only occupies a small area in the entire imaging scene, this sparsity characteristic makes the compressed sensing (CS) technology effective for the reconstruction of high-resolution forward-looking images. Therefore, it is possible to provide forward-looking high-resolution images for small moving platforms by using multi-channel radars combined with sparse reconstruction technology.

[0003] However, in multi-channel radar forward-looking sparse imaging, data extrapolation is involved, and therefore many new problems and challenges are also faced in the design of super-resolution imaging methods. As a key factor affecting the performance of multi-channel radar forward-looking super-resolution imaging, it is crucial to explore methods to suppress strong noise. The sparsity of noise distribution will have a major impact on the reconstruction of target information, which poses a challenge to the technology of realizing forward-looking super-resolution imaging by using sparse reconstruction. The current methods for studying strong noise in radar imaging in the literature can be divided into two categories. The first category of methods performs denoising preprocessing on the echo, and this category of methods uses the characteristics of radar signals to extract the signal in the radar echo, realizing the separation of target signal and noise, but the suppression ability of this category of methods is very limited. The second category of methods adds additional constraints in addition to sparsity, and the low rank constraint is the most widely used method. However, in complex imaging scenes, the low rank characteristics of the data matrix are often not obvious, which limits the effect of super-resolution imaging by directly imposing a low rank constraint. Therefore, how to enhance the low rank characteristics of the constraint matrix is crucial to improving the imaging quality. SUMMARY

[0004] The application provides a multi-channel radar forward-looking imaging method based on sparsity and weighted low rank, solves the problem that in the prior art, under a complex imaging scene, super-resolution imaging effect is limited, and realizes noise suppression through double constraints of sparsity and weighted low rank, and obtains a reconstructed forward-looking high-resolution image with high quality and high resolution.

[0005] The application provides a multi-channel radar forward-looking imaging method based on sparsity and weighted low rank, and the method comprises the following steps:

[0006] An echo signal model under a multi-channel radar single-transmit-single-receive mode is established, and corresponding echo signals of targets are obtained by using the model;

[0007] The echo signals are subjected to signal compression correction processing, and corrected echo signals are obtained;

[0008] The corrected echo signals are expressed as a two-dimensional echo matrix, a target signal matrix in the two-dimensional echo matrix is subjected to weighted processing, and a weighted target signal matrix with a low rank characteristic is obtained; wherein the two-dimensional echo matrix comprises a target signal matrix and a noise matrix;

[0009] A forward-looking imaging model is obtained according to the sparsity of the targets in the echo signal model and the low rank characteristic of the weighted target signal matrix;

[0010] The forward-looking imaging model is solved based on ALM under an ADMM framework, and a reconstructed forward-looking high-resolution image is obtained.

[0011] In a possible implementation manner, the corresponding echo signals of the targets are obtained by using the model, and the method comprises the following steps:

[0012] The distance of a target p from an n th antenna array element in slow time t is determined;

[0013] The target is subjected to coherent demodulation by using a transmission signal and the distance, and the corresponding echo signals of the targets are obtained.

[0014] In a possible implementation manner, the echo signals are expressed as:

[0015]

[0016] Wherein C represents a complex scattering coefficient of a target; w r represents a distance window function; τ represents fast time; R(t) represents the distance of a target p from an n th array antenna element in slow time t; c represents the speed of light; γ represents a frequency modulation slope; and s(τ, t) represents an echo signal with fast time τ and slow time t.

[0017] In a possible implementation, the signal compression correction processing on the echo signal to obtain a corrected echo signal comprises:

[0018] The echo signal is subjected to range direction pulse compression processing to obtain a pulse compressed echo signal.

[0019] A range migration correction factor corresponding to the pulse compressed echo signal is constructed in the frequency domain, and the pulse compressed echo signal is subjected to motion compensation correction to obtain a corrected echo signal.

[0020] In a possible implementation, the weighting processing on the target signal matrix in the two-dimensional echo matrix to obtain a weighted target signal matrix with low rank characteristics comprises:

[0021] An adaptive algorithm based on a linear constraint minimum variance criterion in an ADMM framework is used to solve a covariance matrix of the pulse compressed echo signal corresponding to each array element in the echo signal model to obtain a weighting weight.

[0022] The elements in the target signal matrix are weighted by using the weighting weight to obtain a weighted target signal matrix with low rank characteristics.

[0023] In a possible implementation, the weighting weight is expressed as:

[0024] W = μ (S rc S rc H ) -1 ;

[0025] Wherein, μ represents a constant factor; S rc represents a two-dimensional echo matrix; (·) H represents the conjugate transpose of a matrix.

[0026] In a possible implementation, the corrected echo signal is expressed as:

[0027]

[0028] Wherein, B represents a bandwidth; R(t) represents a distance from a target P to an n th array antenna element at a slow time t; c represents a light speed; C represents a complex scattering coefficient of the target; represents a Doppler frequency caused by equivalent motion of an array antenna; t represents a slow time; exp(·) represents an exponential function; represents a Doppler frequency caused by platform motion; τ represents a fast time; S rc (τ,t) represents a corrected echo signal when the fast time is τ and the slow time is t.

[0029] In a possible implementation, the two-dimensional echo matrix is represented as:

[0030] S rc Y+E=FX+E;

[0031] wherein Y represents a target signal matrix; E represents a noise matrix; F represents a dictionary matrix; and X represents a reconstructed forward-looking high-resolution image.

[0032] In a possible implementation, the forward-looking imaging model is represented as:

[0033]

[0034] wherein W represents a weighted weight; F represents a dictionary matrix; X represents a reconstructed forward-looking high-resolution image; ||·||1 represents an l1 norm; and λ2 represents a second regularization parameter. * rc F *

[0035] In a possible implementation, the forward-looking imaging model is solved based on the ALM under the ADMM framework to obtain the reconstructed forward-looking high-resolution image, and the method comprises the following steps:

[0036] Converting the forward-looking imaging model into a representation form of a Lagrangian function to obtain a first conversion result;

[0037] Alternately updating a plurality of variables in the first conversion result according to an ADMM method until a termination condition is met;

[0038] According to values of the plurality of variables under the termination condition, modifying the first conversion result to obtain a second modification result;

[0039] Solving unknown quantities in the second modification result to obtain the reconstructed forward-looking high-resolution image.

[0040] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0041] The application performs weighted processing on a target signal matrix in a two-dimensional echo matrix to obtain a weighted target signal matrix with a low-rank characteristic, and the target signal matrix is weighted, so that the low-rank characteristic of the target signal matrix is effectively enhanced; through the dual constraints of the sparsity of the target and the low-rank characteristic of the weighted target signal matrix, the noise is suppressed, and a high-quality and high-resolution reconstructed forward-looking high-resolution image is obtained. BRIEF DESCRIPTION OF DRAWINGS ​​​​

[0042] Figure 1 A sparse and weighted low-rank based multi-channel radar forward-looking imaging method step flow chart provided for an embodiment of the present application;

[0043] Figure 2 A small motion platform carries a multi-channel radar "single launch and single receiving" mode forward-looking imaging observation geometry schematic diagram provided for an embodiment of the present application;

[0044] Figure 3a And Figure 3b A maximum pixel pair imaging total energy contribution ratio curve provided for an embodiment of the present application;

[0045] Figure 4 A target signal matrix weighted before and after characteristic value change schematic diagram provided for an embodiment of the present application;

[0046] Figure 5 A simulation experiment scattering point distribution schematic diagram provided for an embodiment of the present application;

[0047] Figure 6 A simulation experiment radar platform parameter schematic diagram provided for an embodiment of the present application;

[0048] Figure 7a An imaging diagram of the same distance unit scattering point obtained by using a real beam method provided for an embodiment of the present application;

[0049] Figure 7b An imaging diagram of the same distance unit scattering point obtained by using a traditional CS imaging method provided for an embodiment of the present application;

[0050] Figure 7c An imaging diagram of the same distance unit scattering point obtained by using a sparse and low-rank imaging method provided for an embodiment of the present application;

[0051] Figure 7d An imaging diagram of the same distance unit scattering point obtained by using the method of the present application provided for an embodiment of the present application;

[0052] Figure 8 A measured experiment scene schematic diagram provided for an embodiment of the present application;

[0053] Figure 9 A measured experiment radar platform parameter schematic diagram provided for an embodiment of the present application;

[0054] Figure 10a A real beam method imaging result diagram provided for an embodiment of the present application;

[0055] Figure 10b A traditional CS method imaging result diagram under low SNR provided for an embodiment of the present application;

[0056] Figure 10c An imaging result diagram of the present application provided by the embodiment of the present application under low SNR and sparse and low rank method;

[0057] Figure 10d An imaging result diagram of the present application provided by the embodiment of the present application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are 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 a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0059] The present application provides a multi-channel radar forward-looking imaging method based on sparse and weighted low rank, referring to Figure 1 The method comprises the following steps S101 to S105.

[0060] S101, an echo signal model under a multi-channel radar single-transmit-single-receive mode is established, and the echo signal s(τ, t) corresponding to each target is obtained by using the model;

[0061] Specifically, in step S101, the echo signal corresponding to each target is obtained by using the model, comprising the following steps S1011 to S1012.

[0062] S1011, the distance R(t) of the target p from the n th antenna element in the echo signal model at the slow time t is determined;

[0063] S1012, the target is coherently demodulated by using the transmitting signal s(τ) and the distance R(t), and the echo signal s(τ, t) corresponding to each target is obtained.

[0064] Exemplarily, referring to Figure 2 , Figure 2 A forward-looking imaging observation geometry schematic diagram of a small motion platform carrying a multi-channel radar under a "single-transmit-single-receive" mode.

[0065] N transceiver antenna elements are uniformly arranged at equal intervals d on a radar platform with a height of H, and the array antenna length is L; wherein the radar platform moves at a speed v, the N antenna elements are switched with a pulse repetition interval (PRI) as a time interval, and the signals are transmitted and the echoes are received in turn, that is, a "single-transmit-single-receive" working mode.

[0066] Suppose there is a target P(x0, y0, 0) in front of the radar platform, the distance R(t) from the target P to the nth array antenna element at the slow time t can be approximately expressed as:

[0067]

[0068] where t represents the slow time; x0 and y0 represent the horizontal coordinate and the vertical coordinate of the target P respectively; v a = d / PRI is the equivalent azimuth motion velocity; is the slant range from the target P to the center of the radar antenna.

[0069] Suppose the radar transmits a linear frequency modulation (LFM) signal s(τ), which is specifically expressed as:

[0070]

[0071] where τ represents the fast time, w r represents the range window function, f c represents the carrier frequency, and γ represents the frequency modulation slope.

[0072] S102, the echo signal s(τ, t) is processed for signal compression correction to obtain the corrected echo signal S rc (τ, t);

[0073] Specifically, in step S102, the echo signal s(τ, t) is processed for signal compression correction to obtain the corrected echo signal S rc (τ, t), including the following steps S1021 to S1022.

[0074] S1021, the echo signal s(τ, t) is processed for range pulse compression to obtain a pulse compressed echo signal S rc1 (τ, t);

[0075] S1022, a distance travel correction factor corresponding to the pulse compressed echo signal S rc (τ, t) is constructed in the frequency domain, and the pulse compressed echo signal S rc1 (τ, t) is processed for motion compensation correction to obtain the corrected echo signal S rc (τ, t).

[0076] Here, the echo signal s(τ, t) is expressed as:

[0077]

[0078] where C represents the complex scattering of the target; w rdenotes a range window function; τ denotes fast time; R(t) denotes the distance from the target P to the nth array antenna element at the slow time t; c denotes the speed of light; γ denotes the frequency modulation slope.

[0079] Here, the corrected echo signal S rc (τ, t) is expressed as:

[0080]

[0081] where B denotes the bandwidth; R(t) denotes the distance from the target P to the nth array antenna element at the slow time t; c denotes the speed of light; C denotes the complex scattering coefficient of the target; denotes the Doppler frequency caused by the equivalent motion of the array antenna; t denotes the slow time; exp(·) denotes the exponential function; denotes the Doppler frequency caused by the platform motion; τ denotes the fast time.

[0082] Exemplarily, the echo signal s(τ, t) is subjected to pulse compression processing, and the echo S rc1 (τ, t) after range direction pulse compression can be expressed as:

[0083]

[0084] where B is the bandwidth, and c is the speed of light.

[0085] A range migration correction factor is constructed in the frequency domain for the echo signal after pulse compression, and a corrected echo signal S rc (τ, t) is obtained to eliminate the influence of the platform motion on the echo.

[0086] S103, the corrected echo signal S rc (τ, t) is expressed as a two-dimensional echo matrix S rc The target signal matrix in the two-dimensional echo matrix S rc is subjected to weighting processing to obtain a weighted target signal matrix with low rank characteristics; wherein the two-dimensional echo matrix S rc includes: a target signal matrix and a noise matrix;

[0087] Here, the two-dimensional echo matrix S rc is expressed as:

[0088] S rc = Y + E = FX + E (6)

[0089] where Y denotes the target signal matrix; E denotes the noise matrix; F denotes the dictionary matrix; X denotes the reconstructed forward-looking high-resolution image.

[0090] Specifically, in step S103, the two-dimensional echo matrix S rcS1031, using an Alternating Direction Method of Multipliers (ADMM) framework, an adaptive algorithm based on a linearly constrained minimum variance criterion is used to solve the weighted weight value of the covariance matrix of the echo signal model of each array element corresponding to the pulse compression echo signal S

[0091] S1031, using an Alternating Direction Method of Multipliers (ADMM) framework, an adaptive algorithm based on a linearly constrained minimum variance criterion is used to solve the weighted weight value of the covariance matrix of the echo signal model of each array element corresponding to the pulse compression echo signal S rc1

[0092] Here, the weighted weight value is represented as:

[0093] W=μ(S rc S rc H ) -1 (7)

[0094] where μ represents a constant factor; S rc represents a two-dimensional echo matrix; (·) H represents the conjugate transpose of the matrix.

[0095] S1032, using the weighted weight value to weight the elements in the target signal matrix, to obtain a weighted target signal matrix.

[0096] For example, after motion compensation, a two-dimensional echo matrix S rc is obtained after pulse compression, where Y∈C N×M and E∈C N ×M are a signal matrix and a noise matrix, respectively, is a dictionary matrix, is a reconstructed forward-looking high-resolution image.

[0097] Adaptive digital beamforming (ADBF) is an adaptive antenna array in a complex interference environment, according to different optimization criteria, and using the corresponding adaptive algorithm, the output of each array element is weighted and summed, so that the array pattern obtains the maximum gain in the expected direction, and generates nulls in the interference direction, so as to enhance the useful signal and suppress the interference.

[0098] wherein the linearly constrained minimum variance (LCMV) is used to solve the weighted weight value as follows:

[0099] ​

[0100] Among them, R X It is the two-dimensional echo matrix S after pulse compression. rc The covariance matrix is ​​given by , where a represents the desired signal steering vector and w represents the adaptive weights to be determined.

[0101] In the multi-channel radar forward-looking imaging of the present invention, the radar beam always points to the target, so the desired guiding vector is an identity matrix. Therefore, the weighting value in the present invention is formula (7).

[0102] S104. Based on the sparsity of the target in the echo signal model and the low-rank characteristic of the weighted target signal matrix, the forward-looking imaging model is obtained.

[0103] Here, the forward-looking imaging model is represented as:

[0104]

[0105] Where W represents the weighting values; F represents the dictionary matrix; X represents the reconstructed front-view high-resolution image; ||·|| * λ represents the nuclear norm; λ1 is the first regularization parameter; ||·||1 represents the l1 norm; λ2 is the second regularization parameter; S rc Represents a two-dimensional echo matrix; ||·|| F Denotes the F-norm; ||WFX|| * Let ||WFX|| denote the nuclear norm of the weighted matrix WFX. * :=Σ i σ i , σ i Let represent the i-th singular value of the weighted matrix WFX.

[0106] For example, in forward-looking image reconstruction, the low-rank property of the weighted target signal matrix Y and the sparsity property of the target are utilized to improve the performance of forward-looking imaging. The forward-looking imaging model with sparsity and weighted low-rank property constraints is as follows:

[0107]

[0108] Where rank(·) denotes the rank function, ||·||0 denotes the l0 norm; λ1 is the first regularization parameter; and λ2 is the second regularization parameter. λ1 is used to balance the l0 norm and the rank function of the weighted matrix WY, and λ2 is a constant related to noise.

[0109] Considering that the l0 norm and the rank(·) function are usually NP-hard problems, the l0 norm is convexly relaxed to the l1 norm, and the rank(·) function is convexly relaxed to the nuclear norm. Then, the jointly constrained forward imaging problem can be represented by the forward imaging model, as Equation (9).

[0110] S105, solving the forward imaging model based on an Augmented Lagrangian Method (ALM) under an ADMM framework to obtain a reconstructed forward high-resolution image.

[0111] Specifically, in step S105, the forward imaging model is solved based on the ALM under the ADMM framework to obtain the reconstructed forward high-resolution image, including steps S1051 to S1054.

[0112] S1051, converting the forward imaging model into a representation of a Lagrangian function to obtain a first conversion result;

[0113] S1052, alternately updating a plurality of variables in the first conversion result according to an ADMM method until a termination condition is met;

[0114] S1053, modifying the first conversion result according to values of the plurality of variables under the termination condition to obtain a second modified result;

[0115] S1054, solving unknown quantities in the second modified result to obtain the reconstructed forward high-resolution image.

[0116] For example, in order to facilitate calculation, first let G1-X=0, G2-X=0, then the ADMM form of the optimization problem is represented as: s.t.

[0117]

[0118] Then, the above formula is represented in the form of an augmented Lagrangian function:

[0119]

[0120] Wherein, Q1, Q2 are Lagrange multiplier matrices, and u1, u2 are penalty factors.

[0121] The variables G1, G2, X, Q1 and Q2 are alternately updated using the ADMM method, that is, one of the variables is optimized while the other variables remain unchanged until a specific convergence condition is reached.

[0122] The values of each variable are updated iteratively, and the k+1 iteration solution of the variables G1, G2 and X is:

[0123]

[0124] Wherein, Soft(·) represents a soft threshold function corresponding to the L1 norm sparse regularization enhancement, and soft(Θ, α) = max(1-α / |Θ(i,j)|, 0)·Θ(i,j), Θ and α are a matrix and a constant, respectively.

[0125] In addition, the Lagrange multiplier matrix and the penalty coefficient need to be iteratively updated, and the Q1 k+1 , Q2 k+1 , u1 k +1 and u2 k+1 The iteration result expression is as follows:

[0126]

[0127] Where ρ is the linear coefficient of the growth of u1 and u2, the size of ρ determines the speed of convergence, and ρ>1.

[0128] Through continuous iteration and solution, the image matrix X is finally obtained, that is, the reconstructed forward-looking high-resolution image.

[0129] In the invention, the forward-looking image reflects the energy distribution of the target scattering field in the range-Doppler two-dimensional plane, and most of the energy is usually contributed by a few strong scattering centers. For example, in the car imaging case shown in Figure 3a , if all the pixels in the imaging plane are sorted and counted according to the amplitude, it can be seen that the energy of 1% of the largest pixels in the imaging plane accounts for more than 99% of the total energy, and 10% of the largest pixels almost contributes all the energy (99.4%). Figure 3b Further reveals the contribution ratio curve of the largest pixels to the total energy of imaging, from which it can be seen that the image usually has significant sparsity. The sparsity of this signal has great advantages for signal processing, which helps to improve the processing efficiency and imaging quality.

[0130] The low-rank property can be verified by the method of singular value decomposition. The singular value distribution of the matrix can reflect whether it has the low-rank property. Therefore, the low-rank property can be verified by observing the attenuation of the singular values of the matrix before and after weighting. Figure 4The scattering point models of one, two and three ships are given respectively, and the corresponding singular value distribution results of the signal matrix are given. It can be observed that the singular value distribution of the original signal matrix presents a decay trend, indicating that it has a low-rank characteristic. However, it is worth noting that the downward curve of the singular value of the original signal matrix presents a bending trend, which indicates that the decay speed of the singular value is relatively slow, especially in the case of increasing the number of targets in the scene. In contrast, after the signal matrix is weighted by the proposed method, the decay speed of its singular value is obviously accelerated, and its downward curve becomes downward bending, highlighting the low-rank characteristic of the signal matrix. Therefore, the performance of radar in the complex imaging field of forward-looking super-resolution imaging can be further improved by weighting the signal matrix.

[0131] The simulation and measured data experiments are used to verify the present application.

[0132] (1) Simulation conditions in simulation experiment:

[0133] Please refer to Figure 5 , Figure 5 Figure 1 is a schematic diagram of ship target distribution in a simulation experiment provided by the present application. A multi-channel radar "single launch and single receive" forward-looking imaging geometry model is selected for simulation experiment, and the scene contains 3 ship targets, and the scattering points of the ship targets are distributed on the grid points. The interval of each grid point in the range direction is 1m, which corresponds to the range resolution; the interval of each grid point in the azimuth direction is 4m, which corresponds to the azimuth resolution. The specific radar platform parameters are shown in Table 1. Figure 6 .

[0134] Simulation content in simulation experiment: In order to verify the anti-noise ability of the proposed imaging method, the super-resolution multiple is set to 8 times (i.e. the antenna length is 0.2m), and Gaussian white noise is added to the radar echo to form data with a signal-to-noise ratio of 5dB. The imaging results of the present application method, the real beam imaging method, the traditional CS imaging method and the sparse and low-rank imaging method under strong noise condition are compared, as shown in Figure 7.

[0135] It can be seen from Figure 7a that the scattering points in the same range unit coincide together and cannot be distinguished, so the real beam method does not have super-resolution imaging ability; it can be seen from Figure 7b that the traditional CS imaging method can distinguish some targets, but it cannot obtain satisfactory high-resolution images under strong noise condition. Similarly, it can be seen from Figure 7c that the traditional sparse and low-rank imaging method has limited noise suppression ability, and can only reconstruct part of the targets and has defocusing problem in the complex imaging environment. In contrast, even under low SNR, the imaging results of the proposed method are clear and distinguishable, as shown in Figure 7dAs shown, this indicates the superior performance of the proposed method.

[0136] (2) Actual measurement experiment, actual measurement condition:

[0137] Please refer to Figure 8 , Figure 8 It is the actual measurement experiment scene schematic diagram provided by the embodiment of the application. The experimental equipment is AWR2243 cascade radar, the experimental site is located in a parking lot of Xi'an University of Electronic Science and Technology, the target is two vehicles, and the specific radar working parameters are shown in Figure 9 .

[0138] Actual measurement experiment, simulation content:

[0139] The imaging results of the method, the real beam imaging method, the traditional CS imaging method and the sparse and low rank imaging method under strong noise condition are compared, as shown in Figure 10.

[0140] From Figure 10a It can be seen that the two cars in the imaging result of the real beam method are fused together and cannot be distinguished at all. From Figure 10b and Figure 10c It can be seen that under low SNR, the images generated by the traditional CS method and the sparse and low rank method are difficult to distinguish the two vehicles, and a large number of false targets appear. The imaging method proposed in the application can produce satisfactory imaging results, and the two cars are clearly distinguishable, as shown in Figure 10d The actual measurement experiment proves that the proposed imaging method not only has super-resolution imaging capability, but also has strong noise resistance.

[0141] In summary, the feasibility of the application is proved by simulation and actual measurement data.

[0142] The application proposes a multi-channel radar forward-looking imaging method based on sparse and weighted low rank to solve the problem of low resolution of the existing forward-looking imaging method, improves the imaging resolution, and has strong noise robustness. The application analyzes the forward-looking imaging scene and the echo matrix respectively, obtains the characteristic that the imaging scene target has sparse distribution, and uses weighting to enhance the low rank characteristic of the signal matrix in the echo matrix, and through the double constraints of sparse and weighted low rank, a forward-looking super-resolution imaging model with noise robustness is obtained.

[0143] The various embodiments in this specification are described in a progressive manner, and the same or similar parts among the various embodiments can be referred to each other, and each embodiment focuses on the difference from other embodiments. The whole or part of the present application can be used in a variety of general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, mobile communication terminals, multi-processor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0144] The above examples are only used to illustrate the technical solutions of the present application, and are not limited to the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the present application.

Claims

1. A multi-channel radar forward-looking imaging method based on sparse and weighted low-rank, characterized in that, include: A model of echo signal in single-transmit and single-receive mode of multi-channel radar is established, and the echo signal corresponding to each target is obtained using this model. The echo signal is subjected to signal compression and correction processing to obtain the corrected echo signal; The corrected echo signal is represented as a two-dimensional echo matrix. The target signal matrix in the two-dimensional echo matrix is ​​weighted to obtain a weighted target signal matrix with low-rank characteristics. The two-dimensional echo matrix includes a target signal matrix and a noise matrix. Based on the sparsity of the target in the echo signal model and the low-rank characteristic of the weighted target signal matrix, a forward-looking imaging model is obtained; the forward-looking imaging model is expressed as: ； in, Indicates the weighted values; Represents a dictionary matrix; This represents the reconstructed front-view high-resolution image; Represents the nuclear norm; This is the first regularization parameter; express Norm; This is the second regularization parameter; Represents a two-dimensional echo matrix; express Norm; Represents the weighted matrix nuclear norm number; The forward-looking imaging model is solved based on ALM within the ADMM framework to obtain a reconstructed high-resolution forward-looking image.

2. The multi-channel radar forward-looking imaging method based on sparse and weighted low-rank as described in claim 1, characterized in that, The process of obtaining the echo signals corresponding to each target using this model includes: Determined in slow time Momentary Goal According to the echo signal model, the first The distance between each antenna element; The target is coherently demodulated using the transmitted signal and the distance to obtain the echo signal corresponding to each target.

3. The multi-channel radar forward-looking imaging method based on sparse and weighted low-rank as described in claim 2, characterized in that, The echo signal is represented as: ； in, Represent the complex scattering system of the target; This represents the distance window function; Indicates a fast time; Indicates slow time Momentary Goal To the The distance between the array antenna elements; Represents the speed of light; Indicates the frequency modulation slope; Indicates fast time as Slow time is The echo signal at that time.

4. The multi-channel radar forward-looking imaging method based on sparse and weighted low-rank as described in claim 1, characterized in that, The step of performing signal compression and correction processing on the echo signal to obtain the corrected echo signal includes: The echo signal is subjected to range pulse compression processing to obtain a pulse-compressed echo signal; A distance travel correction factor corresponding to the pulse compression echo signal is constructed in the frequency domain, and motion compensation correction is performed on the pulse compression echo signal to obtain the corrected echo signal.

5. The multi-channel radar forward-looking imaging method based on sparse and weighted low-rank as described in claim 4, characterized in that, The step of weighting the target signal matrix in the two-dimensional echo matrix to obtain a weighted target signal matrix with low-rank characteristics includes: Using an adaptive algorithm based on the minimum variance criterion of linear constraints under the ADMM framework, the covariance matrix of the pulse compressed echo signal corresponding to each array element in the echo signal model is solved to obtain the weighted weights. The elements in the target signal matrix are weighted using the weighting values ​​to obtain a weighted target signal matrix with low-rank characteristics.

6. The multi-channel radar forward-looking imaging method based on sparse and weighted low-rank as described in claim 5, characterized in that, The weighted values ​​are represented as follows: ； in, Indicates a constant factor; Represents a two-dimensional echo matrix; This represents the conjugate transpose of a matrix.

7. The multi-channel radar forward-looking imaging method based on sparse and weighted low-rank as described in claim 4, characterized in that, The corrected echo signal is represented as follows: ； in, Indicates bandwidth; Indicates slow time Momentary Goal To the The distance between the array antenna elements; Represents the speed of light; Represent the complex scattering system of the target; This represents the Doppler frequency caused by the equivalent motion of the array antenna; Indicates slow time; Represents an exponential function; This represents the Doppler frequency caused by the platform's motion; Indicates a fast time; Indicates fast time as Slow time is The corrected echo signal at that time.

8. The multi-channel radar forward-looking imaging method based on sparse and weighted low-rank as described in claim 1, characterized in that, The two-dimensional echo matrix is ​​represented as follows: ； in, Represents the target signal matrix; Represents the noise matrix; Represents a dictionary matrix; This represents the reconstructed front-view high-resolution image.

9. The multi-channel radar forward-looking imaging method based on sparse and weighted low-rank as described in claim 1, characterized in that, The step of solving the forward-looking imaging model based on ALM within the ADMM framework to obtain a reconstructed high-resolution forward-looking image includes: The forward-looking imaging model is converted into a Lagrangian function representation to obtain the first conversion result; The ADMM method is used to alternately update multiple variables in the first transformation result until the termination condition is met. Based on the values ​​of multiple variables under the termination condition, the first conversion result is modified to obtain a second modified result; Solving for the unknowns in the second modified result yields the reconstructed forward-looking high-resolution image.

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