An echo simulation-based separated sparse SAR imaging method, device and medium

Abstract

The application discloses a kind of based on echo simulation's separate sparse SAR imaging method, device and medium, the method uses different norm to constrain non-sparse target and sparse target respectively, constructs separate imaging mathematical model;Based on separate imaging mathematical model design optimization solving algorithm;According to matching filter operator, design echo simulation operator in algorithm;By iteration, the imaging result of non-sparse target and sparse target in scene is respectively obtained.The separate sparse SAR imaging method based on echo simulation used in the application fully considers the difference between non-sparse target and sparse target in scene, grasps the sparse characteristics of target, separately carries out sparse constraint to non-sparse target and sparse target in scene, and obtains the image of target by algorithm iteration, so that the separate imaging framework proposed in the application can process the echo data containing multiple targets and effectively realize the separate imaging of target, to provide technical support for subsequent target detection and other processing.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of sparse signal processing and microwave imaging, and particularly relates to a separated sparse SAR imaging method, device and medium based on echo simulation. BACKGROUND

[0002] Synthetic Aperture Radar (SAR) is a radar that achieves synthetic aperture through the movement of the radar, obtains narrow beam width, and thus improves the resolution of the radar in the azimuth direction. SAR has been widely used in various fields. The sparse SAR imaging technology developed by combining sparse signal processing technology with SAR imaging has great research value. Sparse SAR imaging technology can improve the signal-to-noise ratio of targets and improve image quality. In addition, sparse SAR imaging technology can effectively reduce the pressure of data transmission and storage, and reduce the cost and resource consumption of the system. At the same time, sparse imaging technology has higher sensitivity and accuracy in target detection and identification, which helps to analyze and monitor targets in complex scenes. Therefore, sparse SAR imaging technology has a wide application prospect in military reconnaissance, disaster monitoring, resource exploration and other fields.

[0003] The sparse imaging method based on the observation matrix cannot realize sparse reconstruction of a large range scene due to the large amount of calculation of the observation matrix, while the sparse imaging method based on the echo simulation operator can realize approximate reconstruction of a large range scene. The sparse SAR imaging method based on regularization realizes sparse recovery of the observed scene by solving the LASSO problem. The modeling process based on the LASSO problem usually only describes the sparsity of a single target, and cannot realize separated imaging of different targets. The sparse separated imaging model introduces Frobenius norm constraint for non-sparse targets compared with the traditional single target modeling method, and can realize separated imaging of non-sparse targets and sparse targets. The present application carries out separated imaging of different targets based on sparse signal processing technology, proposes an algorithm based on the separated sparse SAR imaging model, and the algorithm based on the echo simulation operator can realize accurate recovery and reconstruction of both sparse targets and non-sparse targets. SUMMARY

[0004] The purpose of the present application is to propose a separated sparse SAR imaging method, device and medium based on echo simulation, and to realize imaging of non-sparse targets and sparse targets.

[0005] Technical scheme: The separated sparse SAR imaging method based on echo simulation comprises the following steps:

[0006] (1) Based on the sparsity of different targets, different norms are used as regularization terms to constrain non-sparse targets and sparse targets, and a two-dimensional separated sparse SAR imaging model is constructed;

[0007] (2) The iterative soft thresholding algorithm (IST) is used to obtain the optimal solution of the minimization problem of the two-dimensional separated sparse SAR imaging model by using an alternating iterative strategy;

[0008] (3) According to the characteristics of the matched filter operator, a corresponding echo simulation operator is designed;

[0009] (4) The imaging results of non-sparse targets and sparse targets in the scene are obtained by iteration respectively.

[0010] Further, the step (1) is implemented as follows:

[0011] The two-dimensional separated sparse SAR imaging model contains different targets, and is represented as:

[0012]

[0013] wherein, represents the received echo data containing different targets with a size of M×N, represents non-sparse targets in the observed scene, represents sparse targets in the observed scene, and respectively represent the echo simulation operators of X C and X T , and the corresponding matched filter imaging operators are and N represents a noise matrix;

[0014] Based on the reconstruction error, the Frobenius norm and the l 1，1 norm are used as regularization terms to constrain the background image and the target signal image respectively, and the target is obtained by solving the following regularization problem:

[0015]

[0016] wherein, ||·|| F is the Frobenius norm, ||·|| 1，1 is the joint matrix norm, and its definition is as follows:

[0017]

[0018] wherein, X T (i, j) represents the element of the i-th row and the j-th column of the matrix X T .

[0019] Further, the step (2) is implemented as follows:

[0020] The non-sparse target X in the observed scene is iteratively updated using an optimization algorithm alternatively C The non-sparse target X in the observed scene is iteratively updated using an optimization algorithm alternatively T The problem is solved; wherein the X is updated C The problem is solved; wherein the X is updated T The l is processed using an iterative soft thresholding algorithm IST when 1,1 The norm; the algorithm input echo data Y and the operator And Let the non-sparse target X be sparsely reconstructed And the sparse target X Set the iteration step length u and the error parameter ε, and set the iteration number k = 1;

[0021] When the residual ρ k > ε, the non-sparse target estimation value is calculated:

[0022]

[0023] The sparse target estimation value is calculated:

[0024]

[0025] Wherein, prox μ (·) is the threshold operator of IST, and the residual is obtained by the following expression:

[0026]

[0027] Wherein, J(X C , X T ) is the minimum problem to be solved, and finally the reconstructed non-sparse And the sparse target X

[0028] Further, the step (3) is implemented as follows:

[0029] According to the characteristics of the matched filter imaging operator, a corresponding echo simulation operator is designed, and a back projection algorithm is selected as the matched filter imaging operator, which is represented as:

[0030]

[0031] Wherein, C r (·) represents a range direction pulse compression operation, Ψ represents a phase to be compensated, Represents a phase compensation operation, S a (·) represents an azimuth direction coherent accumulation operation, Represents Hadamard product;

[0032] The inverse imaging operator, that is, the echo simulation operator, derived from the matched filter imaging operator based on the back-projection algorithm is represented as:

[0033]

[0034] Wherein, (·) -1 represents the corresponding inverse operation, (·) * represents the conjugate transpose operation of the matrix.

[0035] Further, the step (4) is implemented as follows:

[0036] The imaging results of the non-sparse target and the sparse target in the scene are respectively obtained by iteration; the echo data containing different targets are taken as input, and the separation imaging of the non-sparse target and the sparse target is realized by the constructed echo simulation-based separation sparse SAR imaging model.

[0037] The storage medium of the application, the storage medium stores a computer program, the computer program is executed by at least one processor to realize the steps of the separation sparse SAR imaging method based on echo simulation as described above.

[0038] The device of the application comprises a memory and a processor, wherein:

[0039] The memory is used for storing a computer program capable of running on the processor;

[0040] The processor is used for executing the steps of the separation sparse SAR imaging method based on echo simulation as described above when the computer program is running.

[0041] Beneficial effects: compared with the prior art, the beneficial effects of the application are: 1. Compared with the traditional sparse imaging method based on the observation matrix, the application is based on the echo simulation operator operation, reduces the calculation amount, and realizes the sparse imaging of a large range of observation scenes; 2. Compared with the existing sparse SAR imaging algorithm, the application realizes the separation imaging of different targets. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The flow chart of the separation sparse SAR imaging method based on echo simulation;

[0043] Figure 2 The target detection result of the application applied to simulation data; wherein, (a) is the imaging result of the non-sparse target; (b) is the imaging result of the sparse target. DETAILED DESCRIPTION

[0044] The application will be further described below with reference to the drawings.

[0045] As Figure 1 shown, the present application proposes a separated sparse SAR imaging model and method based on echo simulation, comprising the following steps:

[0046] Step 1: Establish a separated imaging mathematical model based on target sparsity: based on the sparsity of different targets, different norms are used as regularization terms to constrain non-sparse targets and sparse targets, respectively, to construct a separated imaging mathematical model.

[0047] The two-dimensional separated sparse SAR imaging model containing different targets can be expressed as:

[0048]

[0049] wherein, represents the received echo data containing different targets with the size of MxN, represents the non-sparse targets in the observed scene, represents the sparse targets in the observed scene, and respectively represent the echo simulation operators of X C and X T , and the corresponding matched filter imaging operators can be expressed as and N represents the noise matrix.

[0050] Based on the reconstruction error, the Frobenius norm and the l 1，1 norm are used as the regularization terms for constraining the background image and the target signal image, respectively, and the target can be obtained by solving the following regularization problem:

[0051]

[0052] wherein, ||·|| F is the Frobenius norm, ||·|| 1，1 is the joint matrix norm, and its definition is as follows:

[0053]

[0054] wherein, X T (i, j) represents the element of the ith row and the jth column of the matrix X T .

[0055] Step 2: Design an optimization algorithm for solving the separated imaging model: the iterative shrinkage-threshold (IST) algorithm framework is adopted, and the optimal solution of the separated model minimization problem is obtained by using the strategy of alternating iteration.

[0056] According to the separation optimization model obtained in step 1, an optimization algorithm is used to alternately update X C With X T Solving the problem. Where, update X C When using the gradient descent method, update X T When using the iterative soft threshold algorithm to process l 1，1 Norm. Algorithm input echo data Y and operator And Let the non-sparse target of sparse reconstruction be And the sparse target Initialization is 1, the iteration step is u, the error parameter is ε, and the iteration number k is set to 1.

[0057] When the residual ρ k > ε, the following steps are executed:

[0058] 1) Calculate the non-sparse target estimate value:

[0059]

[0060] 2) Calculate the sparse target estimate value:

[0061]

[0062] Where, prox μ (·) is the threshold operator of IST, and the residual can be obtained by the following expression:

[0063]

[0064] Where, J(X C , X T ) is the minimization problem we solve, and finally the reconstructed non-sparse And sparse target

[0065] Step 3: Design of echo simulation operator in algorithm.

[0066] According to the characteristics of the matched filter imaging operator, the corresponding echo simulation operator is designed, and the back projection algorithm is selected as the matched filter imaging operator, which can be represented as:

[0067]

[0068] Where, C r (·) represents the range pulse compression operation, Ψ represents the phase to be compensated, Represents the phase compensation operation, S a (·) represents the azimuth coherent accumulation operation, Represents the Hadamard product.

[0069] The inverse imaging operator of the matched filter imaging operator based on the back-projection algorithm, that is, the echo simulation operator, can be derived and expressed as:

[0070]

[0071] wherein (·) -1 denotes the corresponding inverse operation, (·) * denotes the conjugate transpose operation of a matrix.

[0072] Step 4: Target separation imaging.

[0073] The imaging results of the non-sparse target and the sparse target in the scene are respectively obtained through iteration. The echo data containing different targets are taken as input, and the separation imaging of the non-sparse target and the sparse target is realized through the constructed echo simulation-based separation sparse SAR imaging model.

[0074] The application further provides a storage medium, wherein the storage medium stores a computer program, and the computer program is executed by at least one processor to implement the steps of the echo simulation-based separation sparse SAR imaging method.

[0075] The application further provides a device, comprising a memory and a processor, wherein the memory is used to store a computer program capable of running on the processor, and the processor is used to execute the steps of the echo simulation-based separation sparse SAR imaging method when the computer program is run.

[0076] In order to verify the effectiveness of the method, a separation sparse SAR imaging model and method based on echo simulation are simulated and verified according to the parameters shown in Table 1.

[0077] Table 1: Simulation parameters of spaceborne SAR

[0078]

[0079] The echo data of the scene are separated and imaged by setting the non-sparse target in the scene to contain a 1000*1000 size random amplitude (range [0.01, 0.1]) and random phase (range [-pi, pi]) and the sparse single-point target to have an amplitude of 1 and a phase of 0. Figure 2 The target detection results of the application applied to the simulation data are as follows: Figure 2 The imaging result of the non-sparse target in (a) is as follows: Figure 2 The imaging result of the sparse target in (b) is as follows: Figure 2 As can be seen from the results given by (a) and (b), the non-sparse target and the sparse target in the scene are both well restored. Therefore, the method proposed in the application can realize the separation imaging of the non-sparse target and the sparse target in the scene.

[0080] The specific embodiments described herein are merely illustrative of the spirit of the application. Various modifications or changes in addition or substitution to the described specific embodiments can be made by those skilled in the art without departing from the spirit of the application or exceeding the scope of the appended claims.

Claims

1. An echo-based simulation-based separated sparse SAR imaging method, characterized in that, The method comprises the following steps: (1) based on the sparsity of different targets, different norms are used as regularization terms to constrain non-sparse targets and sparse targets, and a two-dimensional separated sparse SAR imaging model is constructed; (2) an iterative soft threshold algorithm IST is used to obtain the optimal solution of the minimization problem of the two-dimensional separated sparse SAR imaging model by using an alternating iteration strategy; (3) a corresponding echo simulation operator is designed according to the characteristics of the matched filter operator; (4) the imaging results of non-sparse targets and sparse targets in the scene are obtained by iteration.

2. The echo-model-based separated sparse SAR imaging method according to claim 1, characterized in that, The implementation process of the step (1) is as follows: The two-dimensional separated sparse SAR imaging model contains different targets and is represented as: wherein, represents received echo data containing different targets of size M x N, represents non-sparse targets in the observed scene, represents sparse targets in the observed scene, and represents the echo simulation operator of X C and X T , respectively, whose corresponding matched filter imaging operator is and N represents a noise matrix; Based on the reconstruction error, the Frobenius norm and the l 1，1 norm are used as the regularization term to constrain the background image and the constrained target signal image, respectively. The target is obtained by solving the following regularization problem: where || · || F is the Frobenius norm, || · || 1，1 is the joint matrix norm defined as: where X T (i, j) denotes the element of matrix X T in the i-th row and j-th column.

3. The echo-model-based separated sparse SAR imaging method according to claim 1, characterized in that, The implementation process of the step (2) is as follows: The non-sparse target X in the observation scene is updated alternately and iteratively using an optimization algorithm. C With sparse target X in the observation scene T Solve the problem; where X is updated. C When using gradient descent, update X. T The iterative soft thresholding algorithm IST is used to process l. 1，1 Norm; Algorithm input echo data Y and operator and Let the non-sparse objective of sparse reconstruction be set. and sparse targets The initialization is set to 1, the iteration step size is u, the error parameter is ε, and the iteration number is set to k = 1; When the residual p k ε, a non-sparse target estimate is calculated: The sparse target estimation value is calculated: where prox μ (·) is the threshold operator of IST, the residual is obtained by the following expression: where J(X C , X T ) is the minimization problem to be solved, and the reconstructed non-sparse and sparse target 4. The echo-model-based separated sparse SAR imaging method according to claim 1, characterized in that, The implementation process of the step (3) is as follows: According to the characteristics of the matched filter imaging operator, a corresponding echo simulation operator is designed, and a back projection algorithm is selected as the matched filter imaging operator and is represented as: where C r (·) denotes the range-to-pulse compression operation, Ψ denotes the phase to be compensated, denotes the phase compensation operation, S a (·) denotes the azimuth coherently accumulation operation, o denotes the Hadamard product; The inverse imaging operator of the matched filter imaging operator based on the back projection algorithm, that is, the echo simulation operator, is derived and is represented as: where (·) -1 denotes the corresponding inverse operation, (·) * denotes the conjugate transpose operation of a matrix.

5. The echo-model-based separated sparse SAR imaging method according to claim 1, characterized in that, The implementation process of the step (4) is as follows: The imaging results of non-sparse targets and sparse targets in the scene are obtained by iteration; echo data containing different targets are taken as input, a separated sparse SAR imaging model based on echo simulation is constructed, and separated imaging of non-sparse targets and sparse targets is realized.

6. A storage medium, characterized by The storage medium stores a computer program, and the computer program is executed by at least one processor to realize the steps of the separated sparse SAR imaging method based on echo simulation in any one of claims 1 to 5.

7. An electronic device, comprising: The device comprises a memory and a processor, wherein: The memory is used for storing a computer program capable of running on the processor; The processor is used for executing the steps of the separated sparse SAR imaging method based on echo simulation in any one of claims 1 to 5 when the computer program is run.

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