Sparse SAR (Synthetic Aperture Radar) joint imaging method and device based on multiband data and medium

Abstract

The invention discloses a sparse SAR (Synthetic Aperture Radar) joint imaging method and device based on multiband data and a medium, and the method comprises the steps: constraining multiband data by adopting a sparse model combining a # imgabs0 # norm and a # imgabs1 # norm, and constructing a multiband sparse SAR joint imaging model; solving the sparse SAR joint imaging model of the multi-band data to obtain an optimal solution of a minimization problem of the model; a corresponding echo simulation operator is designed according to the characteristics of the matched filtering operator, and iteration implementation of the algorithm is carried out; and obtaining a multi-band data joint imaging result of the scene through iteration. According to the method, the feature that scene support sets are consistent under different wavebands is considered, the sparse feature of the target is grasped, sparse constraint is performed on the scene, side lobes are reduced, the image of the target is effectively acquired through algorithm iteration, and technical support is provided for subsequent image processing.

Description

Technical Field

[0001] The present invention belongs to the field of sparse signal processing and microwave imaging, and in particular relates to a sparse SAR joint imaging method, device and medium based on multi-band data. Background Art

[0002] Synthetic Aperture Radar (SAR) improves the image resolution in azimuth by using synthetic aperture in azimuth. It has higher imaging quality than traditional radar and is now widely used in many fields. By combining sparse signal processing technology and applying the principle of compressed sensing to the field of SAR processing, it is possible to further improve the imaging quality on the basis of traditional SAR imaging methods. Sparse SAR imaging technology has now achieved downsampling data processing, reduced image calculation, focused imaging of moving targets, deblurred imaging and other fields, and has great application potential.

[0003] Emission signals in different bands can obtain different scattering information of the observation scene. Common bands include L-band, C-band, X-band, Ku-band, P-band, etc. The L-band has a longer wavelength and stronger penetration ability. It can penetrate dense vegetation to obtain surface information and is often used for biomass measurement and forest monitoring. C-band and X-band are widely used for surface monitoring in various environments and weather conditions. They can provide clear images and are suitable for urban planning, topographic mapping and natural resource management. The Ku-band is usually used in airborne systems at altitudes above clouds and rain. It has higher resolution and penetration ability and is suitable for high-precision imaging in complex environments. The P-band SAR system performs well in forest monitoring and can obtain accurate information from the canopy. It is suitable for forest biomass measurement and height map generation. Applying sparse signal processing technology to the joint processing of multi-band data and exploring the information correlation in multi-band data is expected to use data from multiple bands to improve the observation ability of the scene and provide an information basis for subsequent image processing. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to propose a sparse SAR joint imaging method, device and medium based on multi-band data to effectively realize multi-band joint imaging of the scene.

[0005] Technical solution: The sparse SAR joint imaging method based on multi-band data described in the present invention specifically includes the following steps: (1) Construct a sparse SAR joint imaging model for multi-band data; (2) Based on the consistency of the scene support set of multi-band data, use The norm constrains the common part of multi-band image data; based on the sparsity of the scene, the The norm constrains the multi-band image data separately; (3) Solve the sparse SAR joint imaging model of multi-band data and obtain the optimal solution to the minimization problem of the model; (4) Design the corresponding echo simulation operator according to the characteristics of the matched filter operator; (5) Obtain the joint imaging results of multi-band data of the scene through iteration.

[0006] Furthermore, the sparse SAR joint imaging model of the multi-band data in step (1) is: ; in, Respectively The echo data received in two unconnected bands, They represent the backscattering coefficients of the scenes observed in the corresponding bands, Respectively The corresponding echo simulation operator is, represents the noise matrix.

[0007] Furthermore, the implementation process of step (2) is as follows: Based on the consistency of the scene support set of echo data in different bands, the group sparsity constraints are imposed on the common parts of the image; according to the sparse characteristics of the scene, the images of the first band and the second band data are sparsely constrained respectively; the scene is obtained by solving the following regularization problem: ; in, is the regularization parameter, yes norm, for The joint matrix norm of is defined as: ; in, Respectively represent matrices No. Line The elements of the column, and Represents the size of the rows and columns of the matrix respectively.

[0008] Furthermore, the implementation process of step (3) is as follows: According to the sparse SAR joint imaging model obtained in step (1), the structural sparse model solving algorithm is used to update Solve the problem, and the final scene restoration result is ; Algorithm input echo data And the operator , ; Respectively represent the 1st to the Band The echo simulation operator of They are The matched filter imaging operator; let the sparse reconstructed image initial value is initialized to 1, and the sparsity , iterative step length , error parameter , initial value of the number of iterations ; when the residual When , execute steps S1 to S7; until , end the iteration; S1: Calculate the residual: ; S2: Calculate image update value: ; S3: Calculate the regularization parameter: ; S4: Sparse constraints on image update values: ; S5: Calculate the common part: ; S6: Calculate the regularization parameter of the common part: ; S7: Apply group sparse constraints to image update values: ; Finally, the scene imaging results are output .

[0009] Furthermore, the implementation process of step (4) is as follows: 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 is expressed as: ; in, Indicates the corresponding The range-directed pulse compression operation of the band Indicates the corresponding The phase that needs to be compensated for each band, Indicates the corresponding Phase compensation operation of each band, Indicates the corresponding The azimuth coherent accumulation operation of each band is performed. represents the Hadamard product; Based on the back-projection algorithm, the corresponding The matched filter imaging operator of the band derives its inverse imaging operator, that is, the corresponding The echo simulation operator of each band is expressed as: ; in, represents the corresponding inverse operation, Represents the conjugate transpose operation of a matrix.

[0010] Furthermore, the implementation process of step (5) is as follows: The multi-band joint imaging results of the scene are obtained through iteration; the multi-band echo data is taken as input, and the multi-band joint imaging of the scene is realized by constructing a sparse SAR joint imaging model based on echo simulation.

[0011] A storage medium described in the present invention stores a computer program, and when the computer program is executed by at least one processor, the steps of the sparse SAR joint imaging method based on multi-band data as described above are implemented.

[0012] An electronic device according to the present invention comprises a memory and a processor, wherein: A memory for storing computer programs that can be run on the processor; The processor is used to execute the steps of the sparse SAR joint imaging method based on multi-band data as described above when running the computer program.

[0013] Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects: Norm and The sparse SAR joint imaging method based on the norm takes into account the feature that the scene support sets under different bands are consistent, grasps the sparse characteristics of the target, performs sparse constraints on the scene, reduces the sidelobes, and effectively realizes the imaging of the scene through algorithm iteration; the present invention can reduce the amount of data and reduce the requirements for the system load; it combines and processes the echo data of multiple bands, utilizes the information in the multi-band data, realizes the sparse imaging of the scene, and improves the image quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of the sparse SAR joint imaging method based on multi-band data; Figure 2The target detection results of the present invention applied to simulated data; wherein (a) is the imaging result of the back-projection algorithm for X-band data; (b) is the imaging result of the back-projection algorithm for K-band data; and (c) is the multi-band joint imaging result of the scene. DETAILED DESCRIPTION

[0015] The present invention is further described in detail below with reference to the accompanying drawings.

[0016] like Figure 1 As shown, the present invention provides a sparse SAR joint imaging method based on multi-band data, comprising the following steps: Step 1: Construct a sparse SAR joint imaging model for multi-band data: ; in, Respectively The echo data received in two unconnected bands, They represent the backscattering coefficients of the scenes observed in the corresponding bands, Respectively The corresponding echo simulation operator and its corresponding matched filter imaging operator can be expressed as , represents the noise matrix.

[0017] Based on the consistency of the scene support set of echo data in different bands, the group sparsity constraints are imposed on the common parts of the image; according to the sparse characteristics of the scene, the images of the first band and the second band data are sparsely constrained respectively; the scene can be obtained by solving the following regularization problem: ; in, is the regularization parameter, yes norm, for The joint matrix norm of is defined as: ; in, Respectively represent matrices No. Line The elements of the column, and Represent the size of the rows and columns of the matrix respectively.

[0018] Step 2: Based on the algorithm framework for solving structured sparse models, solve the sparse SAR joint imaging model of multi-band data and obtain the optimal solution to the minimization problem of the model.

[0019] According to the sparse SAR joint imaging model obtained in step 1, the structural sparse model solving algorithm is used to update Solve the problem, and the final scene restoration result is ; Algorithm input echo data And the operator , ; Respectively represent the 1st to the Band The echo simulation operator of They are The matched filter imaging operator; let the sparse reconstructed image initial value is initialized to 1, and the sparsity , iterative step length , error parameter , initial value of the number of iterations .

[0020] The algorithm flow is as follows: when the residual , perform the following steps; until , end the iteration: 1) Calculate the residual: ; 2) Calculate the image update value: ; 3) Calculate the regularization parameter: ; 4) Sparse constraints on image update values: ; 5) Calculate the common part: ; 6) Calculate the regularization parameter of the common part: ; 7) Apply group sparse constraints to image update values: ; Finally, the scene imaging results are output .

[0021] Step 3: Construct operator iterative implementation.

[0022] 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 is expressed as: ; in, Indicates the corresponding The range-directed pulse compression operation of the band Indicates the corresponding The phase of the band that needs to be compensated, Indicates the corresponding Phase compensation operation of each band, Indicates the corresponding The azimuth coherent accumulation operation of each band is performed. Denotes the Hadamard product.

[0023] Based on the back-projection algorithm, the corresponding The matched filter imaging operator of the band derives its inverse imaging operator, that is, the corresponding The echo simulation operator of each band is expressed as: ; in, represents the corresponding inverse operation, Represents the conjugate transpose operation of a matrix.

[0024] Step 4: Joint imaging of multi-band data.

[0025] The multi-band joint imaging results of the scene are obtained through iteration. Taking the multi-band echo data as input, the multi-band joint imaging of the scene is realized through the constructed sparse SAR joint imaging model based on echo simulation, thus improving the image quality.

[0026] The present invention also provides a storage medium having a computer program stored thereon, and when the computer program is executed by at least one processor, the steps of the sparse SAR joint imaging method based on multi-band data as described above are implemented.

[0027] The present invention also provides an electronic device, comprising a memory and a processor, wherein: the memory is used to store a computer program that can be run on the processor; the processor is used to execute the steps of the sparse SAR joint imaging method based on multi-band data as described above when running the computer program.

[0028] To verify the effectiveness of the method of the present invention, according to the parameters shown in Table 1, , simulate the echo data of X-band and K-band, and perform target simulation verification on the present invention.

[0029] Table 1 Simulation parameters parameter Numeric X-band operating frequency 9.5 GHz K-band operating frequency 20.3 GHz Azimuth antenna length 0.1 m Signal bandwidth 4.4 GHz Transmit pulse duration 10 ns Equivalent radar speed 250 m / s Slope distance from center of view 500 m Figure 2 This is the point target detection result of the present invention applied to simulated data, and the echo data is randomly downsampled by 50%. Figure 2 (a) is the imaging result of the X-band data back-projection algorithm; Figure 2Middle (b) is the imaging result of the K-band data back-projection algorithm; Figure 2 (c) is the multi-band joint imaging result of the scene. Figure 2 The results show that the multi-band joint imaging method can reduce the impact of energy dispersion caused by data downsampling on the image and improve the scene imaging quality. Therefore, the method proposed in the present invention can realize sparse SAR joint imaging of multi-band scene data.

[0030] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A sparse SAR joint imaging method based on multi-band data, characterized in that: The steps include: (1) Construct a sparse SAR joint imaging model for multi-band data; (2) Based on the consistency of the scene support set of multi-band data, use The norm constrains the common parts of multi-band image data; Based on the scene sparsity, use The norm constrains the multi-band image data separately; (3) Solve the sparse SAR joint imaging model of multi-band data and obtain the optimal solution to the minimization problem of the model; (4) Design the corresponding echo simulation operator according to the characteristics of the matched filter operator; (5) Obtain the joint imaging results of multi-band data of the scene through iteration.

2. The sparse SAR joint imaging method based on multi-band data according to claim 1, characterized in that: The sparse SAR joint imaging model of multi-band data in step (1) is: ； in, Respectively The echo data received in two unconnected bands, They represent the backscattering coefficients of the scenes observed in the corresponding bands, Respectively The corresponding echo simulation operator is, represents the noise matrix.

3. The sparse SAR joint imaging method based on multi-band data according to claim 1, characterized in that: The implementation process of step (2) is as follows: Based on the consistency of the scene support set of echo data in different bands, the group sparsity constraints are imposed on the common parts of the image; according to the sparse characteristics of the scene, the images of the first band and the second band data are sparsely constrained respectively; the scene is obtained by solving the following regularization problem: ； in, is the regularization parameter, yes norm, for The joint matrix norm of is defined as: ； in, Respectively represent matrices No. Line The elements of the column, and Represents the size of the rows and columns of the matrix respectively.

4. The sparse SAR joint imaging method based on multi-band data according to claim 1, characterized in that: The implementation process of step (3) is as follows: According to the sparse SAR joint imaging model obtained in step (1), the structural sparse model solving algorithm is used to update Solve the problem, and the final scene restoration result is ; Algorithm input echo data And the operator , ; Respectively represent the 1st to the Band The echo simulation operator of They are Matched filter imaging operator; Set the initial value of the sparse reconstructed image is initialized to 1, and the sparsity , iterative step length , error parameter , initial value of the number of iterations ; when the residual When , execute steps S1 to S7; until , end the iteration; S1: Calculate the residual: ； S2: Calculate image update value: ； S3: Calculate the regularization parameter: ； S4: Sparse constraints on image update values: ； S5: Calculate the common part: ； S6: Calculate the regularization parameter of the common part: ； S7: Apply group sparse constraints to image update values: ； Finally, the scene imaging results are output .

5. The sparse SAR joint imaging method based on multi-band data according to claim 1, characterized in that: The implementation process of step (4) is as follows: 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 is expressed as: ； in, Indicates the corresponding The range-directed pulse compression operation of the band Indicates the corresponding The phase that needs to be compensated for each band, Indicates the corresponding Phase compensation operation of each band, Indicates the corresponding The azimuth coherent accumulation operation of each band is performed. represents the Hadamard product; Based on the back-projection algorithm, the corresponding The matched filter imaging operator of the band derives its inverse imaging operator, that is, the corresponding The echo simulation operator of each band is expressed as: ； in, represents the corresponding inverse operation, Represents the conjugate transpose operation of a matrix.

6. The sparse SAR joint imaging method based on multi-band data according to claim 1, characterized in that: The implementation process of step (5) is as follows: The multi-band joint imaging results of the scene are obtained through iteration; the multi-band echo data is taken as input, and the multi-band joint imaging of the scene is realized by constructing a sparse SAR joint imaging model based on echo simulation.

7. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by at least one processor, the steps of the sparse SAR joint imaging method based on multi-band data according to any one of claims 1 to 6 are implemented.

8. An electronic device, characterized in that: comprising a memory and a processor, wherein: A memory for storing computer programs that can be run on the processor; A processor is used to execute the steps of the sparse SAR joint imaging method based on multi-band data as described in any one of claims 1 to 6 when running the computer program.

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