A semi-parametric notch filter suppression method for SAR strong narrowband interference

Abstract

The present invention discloses a semi-parametric notch filter suppression method for SAR strong narrowband interference, which includes: obtaining the interfered original SAR image data; performing sparse decomposition on the obtained original SAR image data to obtain a total optimization problem; decomposing the obtained total optimization problem into two sub-optimization problems, wherein the first sub-optimization problem is an optimization problem for solving low-rank interference, and the second sub-optimization problem is an optimization problem for solving sparse useful signals; performing alternating optimization solutions on the two sub-optimization problems to obtain sparse items and low-rank items. The present invention can be directly applied to the original data of a SAR system or a SAR image, and can achieve the effect of suppressing strong narrowband interference in a SAR system and protecting real echoes. Compared with the prior art, the calculation efficiency is higher.

Description

Technical Field

[0001] The invention relates to the technical field of SAR data processing, and in particular to a semi-parametric notch filter suppression method for SAR strong narrowband interference. Background Art

[0002] Synthetic Aperture Radar (SAR) is a microwave sensor that can provide high-resolution images of the observed scene under bad weather and low light conditions. Due to the increasingly crowded spectrum, during the imaging process, the SAR system may be interfered by other systems, such as communication systems and other radar systems, among which there are a lot of strong narrowband interference. Therefore, how to remove or mitigate the strong narrowband interference in the SAR system has attracted great attention from the academic community.

[0003] Currently, there are three traditional methods for removing strong suppression narrowband interference in SAR systems: non-parametric method, parametric method and semi-parametric method.

[0004] For non-parametric methods, although traditional non-parametric methods have the advantages of being simple and fast, and not relying on the waveform characteristics of interference, they will fail when the interference is weak. Therefore, this type of algorithm is not robust enough in a time-varying complex interference environment. At the same time, the signal actually received by the radar is a combination of interference, true echo, and noise. The non-parametric algorithm only considers the intensity characteristics of the interference and lacks constraints on the true echo, making it impossible to effectively protect it. Moreover, when the interference energy is significantly stronger than the echo signal energy, the principal component analysis method in the non-parametric method will also fail.

[0005] The parameterized method can ideally remove the corresponding interference, but it requires the interference to be an isolated single type of interference, and the calculation complexity is high. It also does not take into account the characteristics of the real echo signal and lacks protection for it.

[0006] For the semi-parametric method, it not only uses the optimization model to constrain the interference, but also takes into account the characteristics of the real echo signal. However, it can only target one type of interference at the same time, and the amount of calculation is huge when performing large-scale matrix operations. Summary of the invention

[0007] In view of this, the purpose of the present invention is to provide a semi-parametric notch filtering suppression method for SAR strong narrowband interference, so as to solve the technical problem that there are a large number of strong narrowband interferences in the current SAR system and it is difficult to quickly remove the strong narrowband interference.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] A semi-parametric notch filter suppression method for SAR strong narrowband interference, the method comprising:

[0010] Step S1, obtaining interfered original SAR image data, wherein the original SAR image data has strongly suppressed narrowband interference;

[0011] Step S2: performing sparse decomposition on the original SAR image data obtained in step S1 to obtain an overall optimization problem, wherein the overall optimization problem is: solving the interference of low-rank terms and the useful signal of L1 sparse terms by using the augmented Lagrangian method on the original matrix data;

[0012] In step S2, the expression of the overall optimization problem is:

[0013]

[0014] In formula (1), is the raw data of the SAR image, C is the interference in the form of a low-rank matrix, V is the useful signal in the form of a sparse matrix, λ is a hyperparameter that adjusts the relationship between the solution results C and V, μ is a penalty parameter, Y1 is a Lagrange multiplier, rank(·) represents the rank of the matrix, ‖·‖1 represents the l1 norm of the matrix, ‖·‖ F represents the F norm of the matrix;

[0015] Step S3, decomposing the total optimization problem obtained in step S2 into two sub-optimization problems, wherein the first sub-optimization problem is to solve the optimization problem of low-rank interference, and the second sub-optimization problem is to solve the optimization problem of sparse useful signals;

[0016] In step S3, the specific method for solving the second sub-optimization problem is:

[0017] The specific method for solving the second sub-optimization problem is:

[0018] Formula (2) is solved and is expressed as:

[0019]

[0020] Step S4: performing alternating optimization to solve the two sub-optimization problems in step S3 to obtain sparse terms and low-rank terms;

[0021] The step S4 specifically includes:

[0022] Step S401, initializing algorithm parameters, which includes: setting P = 0, setting V (p) , μ (p) and C (p) The initial value of , where P represents the number of iterations;

[0023] Step S402: Set the set parameter V(p) , μ (p) and Substitute into the first sub-optimization problem and solve the first sub-optimization problem;

[0024] The first sub-optimization problem is solved by using the frequency domain notch filtering method, which specifically includes:

[0025] Step S4021: Perform fast Fourier transform on the distance dimension of formula (3) to obtain F (p) ;

[0026]

[0027] Step S4022: Setting threshold E (p) , F (p) Less than E (p) The data is set to 0, and F1 is obtained (p) ;

[0028] Step S4023: F1 (p) Perform inverse fast Fourier transform in the distance dimension to obtain Z1 (p) ;

[0029] Step S4024: Set C (p+1) Equal to Z1 (p) ;

[0030] Step S403: μ (p) and C (p+1) Substitute into the second sub-optimization problem and solve the second sub-optimization problem; the step S403 specifically includes:

[0031] Will μ (p) and C (p+1) Substitute into formula (2) and solve it to get the sparse term V (p+1) , which includes:

[0032] right Soft thresholding Processing, get V (p+1) ;

[0033] Step S404: Update μ (p+1) , update operation is performed through formula (4) and formula (5);

[0034]

[0035] μ (p+1) =min{ρμ (p) ,μ max} (5)

[0036] In formula (5), μ max and ρ are fixed values ​​set before the cycle;

[0037] Step S405, execute P=P+1;

[0038] Step S406, looping through step S402 to step S405 until convergence is achieved or the maximum number of iterations is reached, outputting C and V, and imaging V as useful data after interference removal to obtain an interference-free SAR image.

[0039] Furthermore, in the step S1, the disturbed original SAR image data is matrix data arranged in the range time domain and the azimuth time domain.

[0040] The beneficial effects of the present invention are:

[0041] The technical solution provided by the present invention can be directly applied to the original data of the SAR system or the SAR image, and can achieve the effect of suppressing strong narrowband interference in the SAR system and protecting the real echo. Compared with the existing technology, its calculation efficiency is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic flow chart of a semi-parametric notch filter suppression method for SAR strong narrowband interference provided in Example 1;

[0043] Figure 2 is the Sentinel-1A original image used in Example 1;

[0044] Figure 3 The image is obtained after the method of this embodiment is executed on the original image. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0046] Example 1

[0047] See also Figure 1-Figure 3 This embodiment provides a semi-parametric notch filter suppression method for SAR strong narrowband interference. The specific process of the method is as follows: Figure 1 The method specifically comprises the following steps:

[0048] Step S1, obtaining interfered original SAR image data, wherein the original SAR image data has strongly suppressed narrowband interference;

[0049] Specifically, in this embodiment, the original SAR image data of Sentinel-1A is read using matlab software. The image has strong narrow-band interference locally. The original image effect is as follows: Figure 2 shown.

[0050] Step S2: performing sparse decomposition on the original SAR image data obtained in step S1 to obtain an overall optimization problem, wherein the overall optimization problem is: solving the interference of low-rank terms and the useful signal of L1 sparse terms by using the augmented Lagrangian method on the original matrix data.

[0051] Specifically, in this embodiment, the expression of the above-mentioned total optimization problem is:

[0052]

[0053] In formula (1), is the raw data of the SAR image, C is the interference in the form of a low-rank matrix, V is the useful signal in the form of a sparse matrix, λ is a hyperparameter that adjusts the relationship between the solution results C and V, μ is a penalty parameter, Y1 is a Lagrange multiplier, rank(·) represents the rank of the matrix, ‖·‖1 represents the l1 norm of the matrix, ‖·‖ F represents the F norm of the matrix;

[0054] Step S3, decomposing the total optimization problem obtained in step S2 into two sub-optimization problems, wherein the first sub-optimization problem is to solve the optimization problem of low-rank interference, and the second sub-optimization problem is to solve the optimization problem of sparse useful signals;

[0055] Specifically, in this embodiment, step S3 includes:

[0056] The specific method for solving the first sub-optimization problem is:

[0057] A frequency domain notch filtering method is performed on the interference C in the form of a matrix of low rank terms.

[0058] The specific method for solving the second sub-optimization problem is:

[0059] Formula (2) is solved and is expressed as:

[0060]

[0061] Step S4: For the two sub-optimization problems in step S3, perform alternating optimization to obtain the sparse term V and the low-rank term C.

[0062] Specifically, in this embodiment, step S4 includes:

[0063] Step S401, initializing algorithm parameters, which includes: setting P = 0, setting V (p) , μ (p) and C (p) The initial value of , where P represents the number of iterations;

[0064] Step S402: Set the set parameter V (p) , μ (p) and Substitute into the first sub-optimization problem and solve the first sub-optimization problem;

[0065] More specifically, in this embodiment, the first sub-optimization problem is solved by using a frequency domain notch filtering method, which specifically includes:

[0066] Step S4021: Perform fast Fourier transform on the distance dimension of formula (3) to obtain F (p) ;

[0067]

[0068] Step S4022: Setting threshold E (p) , F (p) Less than E (p) The data is set to 0, and F1 is obtained (p) , where the threshold E (p) A suitable value entered manually for each iteration;

[0069] Step S4023: F1 (p) Perform inverse fast Fourier transform in the distance dimension to obtain Z1 (p) ;

[0070] Step S4024: Set C (p+1) Equal to Z1 (p) ;

[0071] Step S403: μ (p) and C (p+1) Substitute into the second sub-optimization problem and solve the second sub-optimization problem.

[0072] More specifically, μ (p) and C (p+1) Substitute into formula (2) and solve it to get the sparse term V (p+1) , which includes:

[0073] right Soft thresholding Processing, get V (p+1) ;

[0074] Step S404: Update μ (p+1) , specifically, the update operation is performed by formula (4) and formula (5);

[0075]

[0076] μ (p+1) =min{ρμ (p) ,μ max} (5)

[0077] In formula (5), μ max and ρ are fixed values ​​set before the cycle;

[0078] Step S405, execute P=P+1;

[0079] Step S406, loop through steps S402 to S405 until convergence is achieved or the maximum number of iterations is reached, output C and V, and use V as useful data after interference removal for imaging to obtain a SAR image after interference removal; the imaging effect is as follows: Figure 3 shown.

[0080] In summary, the present invention provides a semi-parametric notch filter suppression method for SAR strong narrowband interference, designs an algorithm that can suppress strong narrowband interference, can be directly applied to the original data of the SAR system or the SAR image, can achieve the effect of suppressing strong narrowband interference in the SAR system and protecting the real echo, and its calculation efficiency is higher than that of the prior art.

[0081] The matters not described in detail in the present invention are all known technologies to those skilled in the art.

[0082] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A semi-parametric notch filter suppression method for SAR strong narrowband interference, characterized in that: The method includes: Step S1, obtaining interfered original SAR image data, wherein the original SAR image data has strongly suppressed narrowband interference; Step S2: performing sparse decomposition on the original SAR image data obtained in step S1 to obtain an overall optimization problem, wherein the overall optimization problem is: solving the interference of low-rank terms and the useful signal of L1 sparse terms by using the augmented Lagrangian method on the original matrix data; In step S2, the expression of the overall optimization problem is: In formula (1), is the raw data of the SAR image, C is the interference in the form of a low-rank matrix, V is the useful signal in the form of a sparse matrix, λ is a hyperparameter that adjusts the relationship between the solution results C and V, μ is a penalty parameter, Y1 is a Lagrange multiplier, rank(·) represents the rank of the matrix, ‖·‖1 represents the l1 norm of the matrix, ‖·‖ F represents the F norm of the matrix; Step S3, decomposing the total optimization problem obtained in step S2 into two sub-optimization problems, wherein the first sub-optimization problem is to solve the optimization problem of low-rank interference, and the second sub-optimization problem is to solve the optimization problem of sparse useful signals; In step S3, the specific method for solving the second sub-optimization problem is: The specific method for solving the second sub-optimization problem is: Formula (2) is solved and is expressed as: Step S4: performing alternating optimization to solve the two sub-optimization problems in step S3 to obtain sparse terms and low-rank terms; The step S4 specifically includes: Step S401, initializing algorithm parameters, which includes: setting P = 0, setting V (p) 、Y1 (P) , μ (p) and C (p) The initial value of , where P represents the number of iterations; Step S402: Set the set parameter V (p) , μ (p) and Y1 (P) Substitute into the first sub-optimization problem and solve the first sub-optimization problem; The first sub-optimization problem is solved by using the frequency domain notch filtering method, which specifically includes: Step S4021: Perform fast Fourier transform on the distance dimension of formula (3) to obtain F (p) ; Step S4022: Setting threshold E (p) , F (p) Less than E (p) The data is set to 0, and F1 is obtained (p) ; Step S4023: F1 (p) Perform inverse fast Fourier transform in the distance dimension to obtain Z1 (p) ; Step S4024: Set C (p+1) Equal to Z1 (p) ; Step S403: Y1 (P) , μ (p) and C (p+1) Substitute into the second sub-optimization problem and solve the second sub-optimization problem; the step S403 specifically includes: Y1 (P) , μ (p) and C (p+1) Substitute into formula (2) and solve it to get the sparse term V (p+1) , which includes: right Soft thresholding Processing, get V (p+1) ; Step S404: Update Y1 (P+1) , μ (p+1) , update operation is performed through formula (4) and formula (5); Y1 (P+1) =Y1 (P) +μ (p) (DC (p+1) -V (p+1) ) (4) m (p+1) =min{ρμ (p) ,m max } (5) In formula (5), μ max and ρ are fixed values ​​set before the cycle; Step S405, execute P=P+1; Step S406, looping through step S402 to step S405 until convergence is achieved or the maximum number of iterations is reached, outputting C and V, and imaging V as useful data after interference removal to obtain an interference-free SAR image.

2. The semi-parametric notch filter suppression method for SAR strong narrowband interference according to claim 1, characterized in that: In the step S1, the disturbed original SAR image data is matrix data arranged in the range time domain and the azimuth time domain.

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