Multi-angle SAR moving target imaging method and device based on compressed sensing algorithm

Abstract

The invention provides a multi-angle SAR moving target imaging method and device based on a compressed sensing algorithm, and the method comprises the steps: carrying out the sub-aperture division of an original radar echo signal, and obtaining a multi-channel original echo signal; the multi-channel original echo signals are preprocessed in a distance Doppler domain, and a moving target imaging model is constructed; a method based on Legendre polynomial fitting is adopted to correct a translational motion error; sparse reconstruction is carried out by adopting an alternating direction multiplier method to obtain a moving target imaging result of a distance Doppler domain, and the two-dimensional space-variant error matrix is introduced into an algorithm framework of the alternating direction multiplier method to compensate a two-dimensional space-variant phase error; and projecting the moving target imaging result of the distance Doppler domain by adopting a two-dimensional interpolation mode according to the relationship between the distance Doppler domain and the northeast coordinate system to obtain a moving target focusing imaging result on the ground distance plane. According to the invention, refocusing imaging, azimuth calibration and attitude correction of the moving target can be effectively realized.

Description

Technical Field

[0001] The present invention relates to the field of radar signal processing, and in particular to a multi-angle SAR moving target imaging method and device based on a compressed sensing algorithm. Background Art

[0002] Synthetic aperture radar ground moving target indication (SAR-GMTI) technology has become a hot topic in the SAR field in recent years. With the deepening of research, its application scope is constantly expanding. In the military field, such as battlefield reconnaissance, intelligence collection, etc.; in the civilian field, such as traffic control in severe weather / dark night conditions, information support for post-disaster rescue, etc. Wide-angle Staring Synthetic Aperture Radar (WasSAR) is a special SAR mode that achieves long-term observation of key areas by flying along a curve or circle. Unlike traditional linear SAR-GMTI (Linear Synthetic Aperture Radar, LSAR-GMTI), WasSAR-GMTI can achieve long-term continuous tracking and monitoring of moving targets. Therefore, WasSAR-GMTI can effectively make up for the shortcomings of traditional linear SAR-GMTI and has a broader application space in military and civilian applications. In WasSAR mode, the complex motion trajectory of the carrier aircraft leads to serious two-dimensional coupling between azimuth and distance. At the same time, the complex maneuvers of non-cooperative targets make it difficult for traditional imaging methods to be applied to moving target imaging in WasSAR mode. Therefore, moving target refocusing imaging in WasSAR mode is still a difficult problem in the field of GMTI. Ground moving target (GMT) imaging can usually be divided into parametric imaging methods and non-parametric imaging methods. Parametric imaging methods refer to the establishment of an accurate SAR moving target imaging model, estimating the velocity parameters or Doppler parameters (center and modulation rate) of the moving target, and then designing a matching function to compensate for the range migration and azimuth defocus caused by the movement. However, most parametric imaging methods are established in LSAR mode. Due to the special motion geometry of WasSAR, there is serious coupling between azimuth and distance. This makes the above methods difficult to apply in WasSAR mode. Non-parametric imaging methods refer to converting the moving target imaging problem into an optimization problem. The method based on inverse synthetic aperture technology (Inverse SAR, ISAR) is an important non-parametric imaging method. ISAR technology can achieve high-precision imaging of non-cooperative moving targets. Its imaging processing does not depend on the estimation results of target motion parameters. It has inherent advantages to apply ISAR technology to WasSAR ground moving target imaging. In recent years, some scholars have combined ISAR imaging with sparse reconstruction algorithms to reconstruct high-resolution ISAR images by solving sparsity-driven optimization problems, and compensate for high-order phase errors by using the low-rank nature of vibration errors or based on image minimum entropy. However, existing methods lack two-dimensional space-variant error compensation methods for complex maneuvering targets. Summary of the invention

[0003] In view of the defects in the prior art, the present invention provides a multi-angle SAR moving target imaging method and device based on a compressed sensing algorithm.

[0004] In order to achieve the above technical objectives, the specific technical solutions adopted by the present invention are as follows: On the one hand, the present invention provides a multi-angle SAR moving target imaging method based on a compressed sensing algorithm, comprising the following steps: Perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal; Preprocessing the multi-channel original echo signals in the range Doppler domain includes: multi-channel registration, clutter suppression and radial velocity estimation; constructing an imaging model for the moving target echo signals obtained after clutter suppression, wherein the moving target imaging model includes non-space-variant translational motion and two-dimensional space-variant rotational motion to be compensated; The translational motion error is corrected using a method based on Legendre polynomial fitting; The alternating direction multiplier method is used for sparse reconstruction to obtain the moving target imaging result in the range Doppler domain. The two-dimensional space-varying error matrix is ​​introduced into the algorithm framework of the alternating direction multiplier method to compensate for the two-dimensional space-varying phase error. According to the relationship between the range Doppler domain and the northeast ground coordinate system, the moving target imaging results in the range Doppler domain are projected using a two-dimensional interpolation method to obtain the moving target focused imaging results on the ground range plane.

[0005] Furthermore, the moving target imaging model is constructed according to the following steps: Perform range pulse pressure and range-direction fast Fourier transform on the multi-channel original echo signals to obtain the target echo baseband signal

[0006] in, , and Represent the bandwidth, carrier frequency and speed of light of the echo signal respectively; Represents fast time, It represents slow time; Represents the aircraft to The slant distance of the scattering center, Indicates the total number of scattering points contained in the moving target; j is an imaginary unit; The target motion is converted into an ideal turntable model according to the following formula

[0007] in, For The instantaneous slant distance change caused by rotation at any moment; and are the abscissa and ordinate of the pth scattering center; is the distance between the reference point and the carrier aircraft; The rotation angle of the ideal arc that the aircraft flies around the origin on the LOS plane with the target center as the origin; is the rotation angle of the moving target around the origin; The carrier to Slope distance of the scattering center Expressed as:

[0008] in, and Represent the radial velocity of the target and the radial velocity of the carrier aircraft respectively; represents translational motion; Will According to Taylor expansion, we get:

[0009] in, , The first Order coefficient; Indicates any time Projection on the reference imaging projection plane; After Taylor expanded Bring in the target's echo baseband signal to get the moving target imaging model: .

[0010] Furthermore, the multi-channel original echo signal is pre-processed in the range Doppler domain, comprising the following steps: Perform fast Fourier transform on the multi-channel original echo signals in the azimuth dimension to achieve azimuth compression and obtain range Doppler echo; A multi-channel moving target detection algorithm is used to process the range Doppler echo to obtain the moving target echo signal after clutter suppression and the target radial velocity estimation result.

[0011] Further, the method for correcting the translational motion error using the method based on Legendre polynomial fitting comprises the following steps: Extracting the moving target echo phase in the moving target imaging model

[0012] in, stands for Phase Extraction; Represents the phase along Untangle; represents fast Fourier transform along fast time; Fitting Phase Error Using Legendre Polynomials

[0013] in, Represents the number of samples along the distance; represents the Legendre polynomial, represents the polynomial coefficients; represents the estimated residual translation error; For distance Sampling frequency points, is the distance sampling frequency; Construct the compensation function: ; The translational motion error is compensated according to the following formula to obtain the moving target imaging model after the translational motion error compensation:

[0014] in, represents the inverse fast Fourier transform along the fast time frequency.

[0015] Furthermore, the sparse reconstruction using the alternating direction multiplier method to obtain the moving target imaging result in the range Doppler domain includes the following steps: The moving target imaging model after translational motion error compensation is divided into blocks along the azimuth dimension in the range Doppler domain.

[0016]

[0017]

[0018] in, Represents the pulse duration; represents the convolution operation; represents the Doppler frequency; represents the phase error in the range Doppler domain; is the window function along the azimuth dimension, represents the azimuth sampling frequency, Represents the number of block points, represents the Doppler frequency interval; Represents the number of blocks; Represents the number of sampling points in azimuth; right Perform inverse fast Fourier transform along the azimuth dimension to obtain the block data in the range, azimuth and time domain signals ; Using the alternating direction multiplication method, Solve the problem and obtain a well-focused sub-block image; The moving target imaging result in the range Doppler domain is obtained by accumulating all the sub-block images.

[0019] Furthermore, the alternating direction multiplier method is used to Solving the problem to obtain a well-focused block sub-image includes the following steps: The solution process is divided into the following three parts: m In the orientation sub-block k +1 cycle can be expressed as:

[0020] in, express The target image can be expressed as ; represents the Fourier matrix along the azimuth direction; stands for conjugate transpose; represents auxiliary variables; represents the dual variable; represents the regularization coefficient; represents the threshold function; represents the space-variant phase compensation matrix; In the formula Expressed as

[0021] in, Representative Range units, phase compensation factor along the azimuth dimension; The phase compensation factor for each distance unit is solved according to the following formula:

[0022]

[0023] in, Representative n An image of distance units; Representative image n Distance unit i Azimuth unit value; Indicates n Range unit echo, Indicates i The Fourier matrix corresponding to each Doppler frequency value; The sharpest image criterion is used to process the phase compensation factor.

[0024] in, Indicates the sharpness of the image; The sharpest image criterion is used to solve the phase compensation factor of each distance unit; Fit the data along the distance direction to obtain the phase error estimate corresponding to all distance units ; Will Bringing into the framework of the alternating direction multiplication method algorithm In the process, the two-dimensional space-variant phase error is corrected, and after multiple iterations of the alternating direction multiplier method, a well-focused block sub-image is obtained.

[0025] Furthermore, the moving target imaging result in the range Doppler domain is expressed as: .

[0026] Furthermore, according to the relationship between the range Doppler domain and the northeast ground coordinate system, the moving target imaging result in the range Doppler domain is projected by a two-dimensional interpolation method to obtain the moving target focused imaging result in the ground range plane, including the following steps: According to the target radial velocity estimate Construct Doppler shift value ; Shift the image along Doppler ; According to the relationship between the range Doppler domain and the northeastern coordinate system, two-dimensional interpolation is used to convert Project to , and obtain the focusing imaging result of the moving target in the ground distance plane.

[0027] Furthermore, the relationship between the range Doppler domain and the northeastern coordinate system is:

[0028] in, , , , They are respectively the aircraft position and speed information recorded by GPS.

[0029] On the other hand, the present invention provides a multi-angle SAR moving target imaging device based on a compressed sensing algorithm, comprising: The first module is used to perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal; The second module is used to pre-process the multi-channel original echo signals in the range Doppler domain, including: multi-channel registration, clutter suppression and radial velocity estimation; construct an imaging model for the moving target echo signals obtained after clutter suppression, and the moving target imaging model includes non-space-variant translational motion and two-dimensional space-variant rotational motion to be compensated; The third module is used to correct the translational motion error by using a method based on Legendre polynomial fitting; The fourth module is used to perform sparse reconstruction using the alternating direction multiplier method to obtain the moving target imaging result in the range Doppler domain, introduce the two-dimensional space-varying error matrix into the alternating direction multiplier method algorithm framework, and compensate for the two-dimensional space-varying phase error; The fifth module is used to project the moving target imaging result in the range Doppler domain by two-dimensional interpolation according to the relationship between the range Doppler domain and the northeast ground coordinate system, so as to obtain the moving target focused imaging result in the ground range plane.

[0030] Compared with the prior art, the present invention has the following beneficial technical effects: The multi-angle SAR moving target imaging method and device based on compressed sensing algorithm provided by the present invention establish a moving target imaging model including non-space-variant translational motion and two-dimensional space-variant rotational motion to be compensated, and adopt a method based on Legendre polynomial fitting to correct the translational motion error; then the moving target imaging model after the translational motion error is compensated is processed in blocks along the azimuth dimension in the range Doppler domain, and for each block sub-image, sparse reconstruction is performed by combining alternating direction sub-multiplication using the sparse characteristic of the moving target, and a space-variant phase compensation matrix is ​​introduced in the reconstruction process, so that in the alternating direction sub-multiplication iterative process, the two-dimensional space-variant phase error is corrected, so that the image signal-to-noise ratio gradually increases with the number of iterations, and the residual space-variant phase error gradually decreases, and finally a well-focused block sub-image is obtained.

[0031] Taking into account that the effective rotation angular velocity of the maneuvering target is time-varying, the present invention utilizes the correspondence between the range Doppler domain and the northeast celestial coordinate system, and adopts two-dimensional interpolation to project the moving target imaging results in the range Doppler domain, thereby solving the azimuth calibration problem and correcting the moving target image to the actual ground plane posture. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0033] Figure 1A flow chart of a multi-angle SAR moving target imaging method based on a compressed sensing algorithm provided by an embodiment; Figure 2 A WasSAR moving target imaging geometry diagram provided by an embodiment; Figure 3 A schematic diagram showing the equivalence between WasSAR moving target imaging and ISAR imaging provided by an embodiment; Figure 4 An optical image of an experimental scene and a SAR imaging result diagram provided by an embodiment; Figure 5 An experimental carrier and carrier GPS recording result diagram provided in an embodiment; Figure 6 An optical image of a cooperative vehicle target provided by an embodiment; Figure 7 Figures of moving target imaging results provided by an embodiment, wherein Figures (a), (f), and (k) are imaging result diagrams of targets 1-3 based on an ISAR imaging method; Figures (b), (g), and (l) are imaging result diagrams of targets 1-3 based on an ISAR imaging method combined with a self-focusing algorithm; Figures (c), (h), and (m) are imaging result diagrams of targets 1-3 based on an alternating direction multiplier method imaging algorithm; Figures (d), (i), and (n) are imaging results of targets 1-3 based on a self-focusing imaging method based on an alternating direction multiplier method; Figures (e), (j), and (o) are imaging result diagrams of targets 1-3 based on a multi-angle SAR moving target imaging method based on a compressed sensing algorithm; Figure 8 A target 1-3 orientation calibration result diagram provided by an embodiment; Fig. 9 An imaging result diagram of three cooperative targets in each sub-aperture and a corresponding optical image are provided in an embodiment. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Reference Figure 1 An embodiment provides a multi-angle SAR moving target imaging method based on a compressed sensing algorithm, comprising the following steps: Perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal; Preprocessing the multi-channel original echo signals in the range Doppler domain includes: multi-channel registration, clutter suppression and radial velocity estimation; constructing an imaging model for the moving target echo signals obtained after clutter suppression, wherein the moving target imaging model includes non-space-variant translational motion and two-dimensional space-variant rotational motion to be compensated; The translational motion error is corrected using a method based on Legendre polynomial fitting; The alternating direction multiplier method is used for sparse reconstruction to obtain the moving target imaging result in the range Doppler domain. The two-dimensional space-varying error matrix is ​​introduced into the algorithm framework of the alternating direction multiplier method to compensate for the two-dimensional space-varying phase error. According to the relationship between the range Doppler domain and the northeast ground coordinate system, the moving target imaging results in the range Doppler domain are projected using a two-dimensional interpolation method to obtain the moving target focused imaging results on the ground range plane.

[0036] Reference Figure 2 , is a geometric diagram of WasSAR moving target imaging provided by an embodiment. In this embodiment, the SAR radar receives the original radar echo signal and performs sub-aperture division to obtain a multi-channel original echo signal; the multi-channel original echo signal is subjected to range pulse pressure and range-direction fast Fourier transform to obtain the target echo baseband signal

[0037] in, , and They represent the bandwidth, carrier frequency and speed of light of the echo signal respectively. Represents fast time, It represents slow time; Represents the aircraft to The slant distance of the scattering center, Indicates the total number of scattering points contained in the moving target; j Is an imaginary unit.

[0038] The carrier aircraft flies along a curve, and the coherent accumulation angle formed relative to the target is the basis for the WasSAR system to achieve high-resolution imaging. Similarly, the rotation of the target relative to the radar is also the essential source of ISAR imaging. Therefore, the ISAR model can be used to solve the imaging problem of moving targets in WasSAR. Existing ISAR models usually decompose the relationship between the radar and the target into translational motion and rotational motion. Translational motion means that the effect on all scattering points on the target is the same, so that different scattering points produce the same Doppler, so it does not contribute to the imaging and will cause image displacement and blur. Without loss of generality, the center of the target is selected as the reference point, and refer to Figure 1 , translational motion Can be expressed as

[0039] in, and Represent the radial velocity of the target and the radial velocity of the carrier respectively. Assuming that the translational motion has been compensated, refer to Figure 3 (a) On the LOS plane, with the target center as the origin, the carrier aircraft flies an ideal arc around the origin; the target motion in the WasSAR mode is converted into an ideal turntable model according to the following formula: Figure 3 (b) As shown:

[0040] in, For The instantaneous slant distance change caused by rotation at any moment; and are the abscissa and ordinate of the pth scattering center; is the distance between the reference point and the carrier aircraft; The rotation angle of the ideal arc that the aircraft flies around the origin on the LOS plane with the target center as the origin; is the rotation angle of the moving target around the origin; The carrier to Slope distance of the scattering center Expressed as:

[0041] in, and Represent the radial velocity of the target and the radial velocity of the carrier aircraft respectively; represents translational motion; However, in reality, the goal is not to make an ideal smooth motion, but to be in a maneuvering motion; maneuvering motion refers to the time-varying rotation speed, rotation acceleration, etc. According to Taylor expansion, we get:

[0042] in, , The first Order coefficient; Indicates any time Projection on the reference imaging projection plane; It can be seen from the above formula that there is range migration in the envelope term, which needs to be corrected by the envelope alignment algorithm. The first phase term is a constant term and can be ignored; the second phase term represents the translational phase error introduced by translational motion, which is not space-variant; the third and fourth phase terms represent the range and azimuth space-variant phase errors, respectively. It can be seen that compared with traditional ISAR imaging, the moving target imaging in WasSAR mode has serious translational errors and two-dimensional space-variant phase errors.

[0043] After Taylor expanded Bring in the target's echo baseband signal to get the moving target imaging model: ; It can be seen from the above that the moving target imaging model includes non-space-variant translational motion and two-dimensional space-variant rotational motion that need to be compensated.

[0044] The multi-channel original echo signal is subjected to fast Fourier transform in the azimuth dimension to achieve azimuth compression and obtain the range Doppler echo. The delay difference caused by the antenna spacing when each channel observes the same scene destroys the correlation of the multi-channel received signals, so the delay error needs to be compensated. With the increase of spatial degrees of freedom, the effect of multi-channel clutter suppression is getting better and better. Multi-channel moving target detection methods include phase center offset antenna, along-track interferometry, clutter interference suppression and space-time adaptive processing. In addition, the radial velocity of the moving target can be estimated by weighted adaptive matched filtering and subspace projection.

[0045] A multi-channel moving target detection algorithm is used to process the range Doppler echo to obtain the moving target echo signal after clutter suppression and the target radial velocity estimation result.

[0046] The method for correcting translational motion error using Legendre polynomial fitting comprises the following steps: Extracting the moving target echo phase in the moving target imaging model

[0047] in, stands for Phase Extraction; Represents the phase along Untangle; represents fast Fourier transform along fast time; Fitting Phase Error Using Legendre Polynomials

[0048] in, Represents the number of samples along the distance; represents the Legendre polynomial, Represents the polynomial coefficients. represents the estimated residual translation error; For distance Sampling frequency points, is the distance sampling frequency; Construct the compensation function: ; The translational motion error is compensated according to the following formula to obtain the moving target imaging model after the translational motion error compensation:

[0049] in, represents the inverse fast Fourier transform along the fast time frequency.

[0050] The method of using the alternating direction multiplier method to perform sparse reconstruction to obtain a moving target imaging result in the range Doppler domain includes the following steps: Since the phase error has a space-varying phase error along the azimuth direction, the moving target imaging model after the translational motion error compensation is processed in blocks along the azimuth dimension in the range Doppler domain. Along To split into blocks:

[0051]

[0052]

[0053] in, Represents the pulse duration; represents the convolution operation; represents the Doppler frequency; represents the phase error in the range Doppler domain; is the window function along the azimuth dimension, represents the azimuth sampling frequency, Represents the number of block points, represents the Doppler frequency interval; Represents the number of blocks; Represents the number of sampling points in azimuth; right Perform inverse fast Fourier transform along the azimuth dimension to obtain the block data in the range, azimuth and time domain signals ; Using the alternating direction multiplication method, Solving the problem to obtain a well-focused block sub-image includes the following steps: The solution process is decomposed into the following three parts, and in order to speed up the operation efficiency, the vector is converted into a matrix, where m In the orientation sub-block k+1 cycle can be expressed as:

[0054] in, express The target image can be expressed as ; represents the Fourier matrix along the azimuth direction; stands for conjugate transpose; represents auxiliary variables; represents the dual variable; represents the regularization coefficient; represents the threshold function; represents the space-variant phase compensation matrix; In the above formula, in order to preserve the moving target structure, the fixed Change to Compared with the traditional alternating direction multiplier method, the space-variant phase compensation matrix is ​​introduced in the above formula to reduce the two-dimensional space-variant phase error.

[0055] In the formula Expressed as

[0056] in, Representative Range units, phase compensation factor along the azimuth dimension; The phase compensation factor for each distance unit is solved according to the following formula:

[0057]

[0058] in, Representative n An image of distance units; Representative image n Distance unit i Azimuth unit value; Indicates n Range unit echo, Indicates i The Fourier matrix corresponding to each Doppler frequency value; The sharpest image criterion is used to process the phase compensation factor.

[0059] in, Indicates the sharpness of the image; Using the sharpest image criterion, the phase compensation factor of each distance unit is solved; when After processing the data of each distance unit, the data is fitted along the distance direction to obtain the phase error estimation value corresponding to all distance units. ; Will Bringing into the framework of the alternating direction multiplication method algorithm In the process, the two-dimensional space-variant phase error is corrected, and after multiple iterations of the alternating direction multiplier method, a well-focused block sub-image is obtained.

[0060] By using multiple iterations of the alternating direction multiplier method, the image signal-to-noise ratio gradually increases (signal-to-noise ratio = moving target signal energy / (residual stationary target signal energy + noise)), and the residual space-varying phase error gradually decreases.

[0061] All the sub-block images are accumulated to obtain the moving target imaging result in the range Doppler domain, and the moving target imaging result in the range Doppler domain is expressed as: .

[0062] The method of projecting the moving target imaging result in the range Doppler domain by a two-dimensional interpolation method according to the relationship between the range Doppler domain and the northeast ground coordinate system to obtain the moving target focused imaging result in the ground range plane includes the following steps: According to the target radial velocity estimate Construct Doppler shift value ; Shift the image along Doppler ; According to the relationship between the range Doppler domain and the northeastern coordinate system, two-dimensional interpolation is used to convert Project to , and obtain the focusing imaging result of the moving target in the ground distance plane.

[0063] The relationship between the range Doppler domain and the northeast coordinate system is:

[0064] in, , , , They are respectively the aircraft position and speed information recorded by GPS.

[0065] In one embodiment, a Ku-band three-channel airborne WasSAR experiment was carried out to verify the effectiveness of the multi-angle SAR moving target imaging method based on the compressed sensing algorithm.

[0066] The experimental scene is a roundabout. The optical and SAR images of the scene are as follows: Figure 4 The radar system is installed on the CESSNA-172R (as shown in Figure 5 (a)), the trajectory of the aircraft is as follows Figure 5 (b) As shown in the experiment, three pickup trucks were arranged as experimental cooperation targets. Figure 4 (a) shows part of the trajectory recorded by the vehicle’s GPS. Figure 6 The front, parking and body details of the pickup truck are shown. The three vehicles are of the same model and size. . Pickup trucks have obvious dihedral structures at the front and parking spaces, which facilitates the subsequent verification of the size and attitude of moving targets. During the experiment, it was considered that a too small sub-aperture would result in low imaging resolution, and a too large sub-aperture would result in complex movement of the moving target during the coherent accumulation time. The complete echo data was divided according to the sub-aperture of 2°, and the sub-aperture overlap rate was 50%.

[0067] In order to verify the effectiveness of the method described in the present invention, the multi-angle SAR moving target imaging method based on the compressed sensing algorithm is compared with the imaging method based on ISAR, the imaging method based on ISAR combined with the autofocus algorithm, the imaging algorithm based on the alternating direction multiplier method, and the autofocus imaging method based on the alternating direction multiplier method. Figure 7The moving target imaging result diagrams provided for an embodiment, wherein Figures (a), (f), and (k) are imaging result diagrams of targets 1-3 based on an ISAR imaging method; Figures (b), (g), and (l) are imaging result diagrams of targets 1-3 based on an ISAR imaging method combined with an autofocusing algorithm; Figures (c), (h), and (m) are imaging result diagrams of targets 1-3 based on an alternating direction multiplier method imaging algorithm; Figures (d), (i), and (n) are imaging results of targets 1-3 based on an autofocusing imaging method based on an alternating direction multiplier method; Figures (e), (j), and (o) are imaging result diagrams of targets 1-3 based on a multi-angle SAR moving target imaging method based on a compressed sensing algorithm. By comparison, it can be seen that in addition to compensating for translational errors, the ISAR-based imaging method does not perform high-order phase compensation for rotational errors, resulting in severe defocusing along the azimuth. The high-order phase error of the target can be effectively compensated based on the ISAR imaging method combined with an autofocusing algorithm, but there are problems such as low image signal-to-noise ratio and unclear target contours. The imaging algorithm based on the alternating direction multiplier method can utilize the sparse characteristics of the target to perform image enhancement and enhance the image signal-to-noise ratio, but this method essentially does not estimate and compensate for the phase error, so the defocus is still serious. Although the self-focusing imaging method based on the alternating direction multiplier method estimates and compensates for the phase error, the focusing quality is low because the two-dimensional spatial variability of the phase error is not considered. The multi-angle SAR moving target imaging method based on the compressed sensing algorithm provided by the present invention combines the advantages of the alternating direction multiplier method for image enhancement, and introduces a two-dimensional phase error compensation method to effectively focus on the target, so that a moving target imaging result with high focusing quality can be obtained, and the posture of the moving target imaging result also corresponds to the actual posture.

[0068] Since the imaging result is on the Los plane, it is different from the actual vehicle shape and posture. The method proposed in this paper is used to perform orientation calibration and posture correction. The results are as follows: Figure 8 First, the size of the vehicle imaging result ( , as well as ) and the actual size of the pickup truck ( ) is basically consistent, and secondly, the vehicle posture is consistent with the actual posture. From the imaging results, it can be seen that due to the dihedral structure in the front and cargo box of the pickup truck, a strong energy area is formed in the SAR image.

[0069] The proposed algorithm is used to refocus the three cooperative moving targets in 200 frames of sub-aperture data. The imaging results are shown in the figure. Fig. 9The proposed algorithm can be used to obtain moving target imaging results with high focusing quality. At the same time, the posture of the moving target imaging result also corresponds to the actual posture, which proves the effectiveness of the algorithm in moving target refocusing imaging, azimuth calibration and posture correction.

[0070] In another embodiment, a multi-angle SAR moving target imaging device based on a compressed sensing algorithm is provided, comprising: The first module is used to perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal; The second module is used to pre-process the multi-channel original echo signals in the range Doppler domain, including: multi-channel registration, clutter suppression and radial velocity estimation; construct an imaging model for the moving target echo signals obtained after clutter suppression, and the moving target imaging model includes non-space-variant translational motion and two-dimensional space-variant rotational motion to be compensated; The third module is used to correct the translational motion error by using a method based on Legendre polynomial fitting; The fourth module is used to perform sparse reconstruction using the alternating direction multiplier method to obtain the moving target imaging result in the range Doppler domain, introduce the two-dimensional space-varying error matrix into the alternating direction multiplier method algorithm framework, and compensate for the two-dimensional space-varying phase error; The fifth module is used to project the moving target imaging result in the range Doppler domain by two-dimensional interpolation according to the relationship between the range Doppler domain and the northeast ground coordinate system, so as to obtain the moving target focused imaging result in the ground range plane.

[0071] Matters not covered by the present invention are known technologies.

[0072] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-angle SAR moving target imaging method based on a compressed sensing algorithm, characterized in that: The following steps are involved: Perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal; Preprocessing the multi-channel original echo signals in the range Doppler domain includes: multi-channel registration, clutter suppression and radial velocity estimation; constructing an imaging model for the moving target echo signals obtained after clutter suppression, wherein the moving target imaging model includes non-space-variant translational motion and two-dimensional space-variant rotational motion to be compensated; The translational motion error is corrected using a method based on Legendre polynomial fitting; The alternating direction multiplier method is used for sparse reconstruction to obtain the moving target imaging result in the range Doppler domain. The two-dimensional space-varying error matrix is ​​introduced into the algorithm framework of the alternating direction multiplier method to compensate for the two-dimensional space-varying phase error. According to the relationship between the range Doppler domain and the northeast ground coordinate system, the moving target imaging results in the range Doppler domain are projected using a two-dimensional interpolation method to obtain the moving target focused imaging results on the ground range plane.

2. The multi-angle SAR moving target imaging method based on compressed sensing algorithm according to claim 1, characterized in that: The moving target imaging model is constructed according to the following steps: Perform range pulse pressure and range-direction fast Fourier transform on the multi-channel original echo signals to obtain the target echo baseband signal in, , and Represent the bandwidth, carrier frequency and speed of light of the echo signal respectively; Represents fast time, It represents slow time; Represents the aircraft to The slant distance of the scattering center, Indicates the total number of scattering points contained in the moving target; j is an imaginary unit; The target motion is converted into an ideal turntable model according to the following formula in, For The instantaneous slant distance change caused by rotation at any moment; and are the abscissa and ordinate of the pth scattering center; is the distance between the reference point and the carrier aircraft; The rotation angle of the ideal arc that the aircraft flies around the origin on the LOS plane with the target center as the origin; is the rotation angle of the moving target around the origin; The carrier to Slope distance of the scattering center Expressed as: in, and Represent the radial velocity of the target and the radial velocity of the carrier aircraft respectively; represents translational motion; Will According to Taylor expansion, we get: in, , The first Order coefficient; Indicates any time Projection on the reference imaging projection plane; After Taylor expanded Bring in the target's echo baseband signal to get the moving target imaging model: 。 3. The multi-angle SAR moving target imaging method based on compressed sensing algorithm as claimed in claim 2, characterized in that: The multi-channel original echo signal is pre-processed in the range Doppler domain, comprising the following steps: Perform fast Fourier transform on the multi-channel original echo signals in the azimuth dimension to achieve azimuth compression and obtain range Doppler echo; A multi-channel moving target detection algorithm is used to process the range Doppler echo to obtain the moving target echo signal after clutter suppression and the target radial velocity estimation result.

4. The multi-angle SAR moving target imaging method based on compressed sensing algorithm according to claim 1, characterized in that: The method for correcting translational motion error using Legendre polynomial fitting comprises the following steps: Extracting the moving target echo phase in the moving target imaging model in, stands for Phase Extraction; Represents the phase along Untangle; represents fast Fourier transform along fast time; Fitting Phase Error Using Legendre Polynomials in, Represents the number of samples along the distance; represents the Legendre polynomial, represents the polynomial coefficients; represents the estimated residual translation error; For distance Sampling frequency points, is the distance sampling frequency; Construct the compensation function: ； The translational motion error is compensated according to the following formula to obtain the moving target imaging model after the translational motion error compensation: in, represents the inverse fast Fourier transform along the fast time frequency.

5. The multi-angle SAR moving target imaging method based on compressed sensing algorithm according to claim 1, characterized in that: The method of using the alternating direction multiplier method to perform sparse reconstruction to obtain a moving target imaging result in the range Doppler domain includes the following steps: The moving target imaging model after translational motion error compensation is divided into blocks along the azimuth dimension in the range Doppler domain. in, Represents the pulse duration; represents the convolution operation; represents the Doppler frequency; represents the phase error in the range Doppler domain; is the window function along the azimuth dimension, represents the azimuth sampling frequency, Represents the number of block points, represents the Doppler frequency interval; Represents the number of blocks; Represents the number of sampling points in azimuth; right Perform inverse fast Fourier transform along the azimuth dimension to obtain the block data in the range, azimuth and time domain signals ; Using the alternating direction multiplier method, Solve the problem and obtain a well-focused sub-block image; The moving target imaging result in the range Doppler domain is obtained by accumulating all the sub-block images.

6. The multi-angle SAR moving target imaging method based on compressed sensing algorithm as claimed in claim 5, characterized in that: The alternating direction multiplier method is used to Solving the problem to obtain a well-focused block sub-image includes the following steps: The solution process is divided into the following three parts: m In the orientation sub-block k +1 cycle can be expressed as: in, express The target image can be expressed as ; represents the Fourier matrix along the azimuth direction; stands for conjugate transpose; represents auxiliary variables; represents the dual variable; represents the regularization coefficient; represents the threshold function; represents the space-variant phase compensation matrix; In the formula Expressed as in, Representative Range units, phase compensation factor along the azimuth dimension; The phase compensation factor for each distance unit is solved according to the following formula: in, Representative n An image of distance units; Representative image n Distance unit i Azimuth unit value; Indicates n Range unit echo, Indicates i The Fourier matrix corresponding to each Doppler frequency value; The sharpest image criterion is used to process the phase compensation factor. in, Indicates the sharpness of the image; The sharpest image criterion is used to solve the phase compensation factor of each distance unit; Fit the data along the distance direction to obtain the phase error estimate corresponding to all distance units ; Will Bringing into the framework of the alternating direction multiplication method algorithm In the process, the two-dimensional space-variant phase error is corrected, and after multiple iterations of the alternating direction multiplier method, a well-focused block sub-image is obtained.

7. The multi-angle SAR moving target imaging method based on compressed sensing algorithm as claimed in claim 5, characterized in that: The moving target imaging result in the range Doppler domain is expressed as: 。 8. The multi-angle SAR moving target imaging method based on compressed sensing algorithm according to claim 1, characterized in that: The method of projecting the moving target imaging result in the range Doppler domain by a two-dimensional interpolation method according to the relationship between the range Doppler domain and the northeast ground coordinate system to obtain the moving target focused imaging result in the ground range plane includes the following steps: According to the target radial velocity estimate Construct Doppler shift value ； Shift the image along Doppler ； According to the relationship between the range Doppler domain and the northeastern coordinate system, two-dimensional interpolation is used to convert Project to , and obtain the focusing imaging result of the moving target in the ground distance plane.

9. The multi-angle SAR moving target imaging method based on compressed sensing algorithm as claimed in claim 8, characterized in that: The relationship between the range Doppler domain and the northeast coordinate system is: in, , , , They are respectively the aircraft position and speed information recorded by GPS.

10. A multi-angle SAR moving target imaging device based on a compressed sensing algorithm, characterized in that: include: The first module is used to perform sub-aperture division on the original radar echo signal to obtain a multi-channel original echo signal; The second module is used to pre-process the multi-channel original echo signals in the range Doppler domain, including: multi-channel registration, clutter suppression and radial velocity estimation; construct an imaging model for the moving target echo signals obtained after clutter suppression, and the moving target imaging model includes non-space-variant translational motion and two-dimensional space-variant rotational motion to be compensated; The third module is used to correct the translational motion error by using a method based on Legendre polynomial fitting; The fourth module is used to perform sparse reconstruction using the alternating direction multiplier method to obtain the moving target imaging result in the range Doppler domain, introduce the two-dimensional space-varying error matrix into the alternating direction multiplier method algorithm framework, and compensate for the two-dimensional space-varying phase error; The fifth module is used to project the moving target imaging result in the range Doppler domain by two-dimensional interpolation according to the relationship between the range Doppler domain and the northeast ground coordinate system, so as to obtain the moving target focused imaging result in the ground range plane.

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