A semi-parametric fast suppression method for SAR system radio frequency interference

Abstract

The present invention provides a semi-parametric fast suppression method for radio frequency interference of a SAR system, comprising the following steps: step 1, sparsely decomposing the SAR image matrix data S and converting it into an optimization problem; step 2, dividing the optimization problem in step 1 into two sub-optimization problems, namely, an optimization problem for solving a low-rank term L and an optimization problem for solving a sparse term M; step 3, alternately optimizing and solving the optimization problem of M and the optimization problem of L to obtain a sparse term M and a low-rank term L, outputting a useful signal of the sparse term M, and obtaining a SAR image after interference removal. The method of the present invention can be directly applied to the original data of the SAR system or the SAR image, and on this basis, the calculation efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the field of SAR data processing, and in particular relates to a semi-parametric fast suppression method for radio frequency interference of a SAR system. Background Art

[0002] Synthetic aperture radar (SAR) is a microwave sensor that can provide high-resolution images of the observed scene without considering weather and lighting. Due to the increasingly crowded spectrum, SAR systems may be interfered with by other systems during the imaging process, such as communication systems and other radar systems. According to the bandwidth of these interferences, they are divided into narrowband interference (NBI) and wideband interference (WBI). Therefore, how to remove or mitigate radio frequency interference in SAR systems has attracted great attention from the academic community.

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

[0004] As 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.

[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.

[0007] For almost all traditional methods, when dealing with broadband interference, each snapshot signal is converted into the time-frequency domain for processing. Due to the large number of snapshot signals, performing short-time Fourier transform (STFT) on each snapshot signal into the time-frequency domain will greatly increase the complexity of the calculation. Summary of the invention

[0008] In order to solve the problem that there is a large amount of radio frequency interference in the current SAR system, the rapid removal of radio frequency interference requires large amount of calculation and low calculation accuracy. The present invention proposes a semi-parametric rapid suppression method for radio frequency interference in the SAR system.

[0009] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0010] A semi-parametric rapid suppression method for radio frequency interference in a SAR system of the present invention comprises the following steps:

[0011] Step 1: sparsely decompose the SAR image matrix data S and transform it into solving the optimization problem

[0012] in, is the raw data of the SAR image, L is the interference in the form of a low-rank matrix, M is the useful signal in the form of a sparse matrix, δ is the allowable noise error, λ is the hyperparameter for adjusting the relationship between the solution results L and M, μ is the penalty parameter, and Y 1 is the Lagrange multiplier, ‖·‖ * represents the nuclear norm of the matrix, ‖·‖ 1 represents the l1 norm of the matrix, ‖·‖ F Represents the F-norm of the matrix.

[0013] Step 2: divide the optimization problem in step 1 into two sub-optimization problems, namely, solving the optimization problem of the low-rank term L and the optimization problem of the sparse term M;

[0014] The optimization problem of solving the sparse term M is expressed as:

[0015]

[0016] The optimization problem of solving the low-rank term L is expressed as;

[0017]

[0018] strank(L) = r;

[0019] Where r represents the rank of the low-rank term L;

[0020] Step 3, alternately optimize and solve the optimization problem of M and the optimization problem of L to obtain the sparse term M and the low-rank term L, output the useful signal of the sparse term M, and obtain the SAR image after interference removal.

[0021] The beneficial effects of the present invention are as follows: the original signal is processed by the semi-parametric suppression method, thereby ultimately achieving the effects of suppressing radio frequency interference, improving operation speed, and protecting real echoes in the SAR system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention is described in more detail below to better reveal the features of the present invention, as shown in the accompanying drawings. The accompanying drawings are only one example of the application of the present invention and do not constitute an improper limitation to the present invention.

[0023] Figure 1 is a flow chart of the method of the present invention;

[0024] Figure 2 is the first Sentinel-1A original image used in the example of the present invention;

[0025] Figure 3 is the image after the first Sentinel-1A original image is processed by the present invention;

[0026] Figure 4 is the second Sentinel-1A original image used in the examples of the present invention;

[0027] Figure 5 This is the image after the second Sentinel-1A original image is processed by the present invention. DETAILED DESCRIPTION

[0028] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] like Figure 1 and Figure 3 As shown, the SAR image data of Sentinel-1A is selected to implement the present invention. A semi-parametric rapid suppression method for radio frequency interference in a SAR system of the present invention comprises the following steps:

[0030] Step 1: Use Matlab software to read the original SAR image data of Sentinel-1A. There is strong radio frequency interference in some parts of the image. The original image effect is as follows: Figure 2 shown.

[0031] The signal S is sparsely decomposed and transformed into an optimization problem

[0032] in, is the raw data of the SAR image, L is the interference in the form of a low-rank matrix, M is the useful signal in the form of a sparse matrix, δ is the allowable noise error, λ is the hyperparameter for adjusting the relationship between the solution results L and M, μ is the penalty parameter, and Y 1 is the Lagrange multiplier, ‖·‖ * represents the nuclear norm of the matrix, ‖·‖ 1 represents the l1 norm of the matrix, ‖·‖ F Represents the F-norm of the matrix.

[0033] Step 2, dividing the optimization problem of step 1 into two sub-optimization problems to be solved; respectively solving the optimization problems of the low-rank term L and the sparse term M;

[0034] The optimization problem of solving the sparse term M is expressed as:

[0035]

[0036] The optimization problem of solving the low-rank term L is expressed as;

[0037]

[0038] strank(L)=r

[0039] Where r represents the rank of the low-rank term L;

[0040] Step 3, alternately optimizing and solving the optimization problem of M and the optimization problem of L to obtain the sparse term M and the low-rank term L; specifically, the steps include:

[0041] Step 3.1, set P = 0, P represents the number of iterations, set C (p) 、V (p) , μ (p) and L (p) The initial value of

[0042] Step 3.2, M (p) , μ (p) and Substitute into the optimization problem to solve the low-rank term L;

[0043] The specific steps are as follows:

[0044] Step 3.2.1, perform QR decomposition on the following disclosure to obtain Q 1 , Q 2 , R 1 and R 2 , the specific formula is:

[0045]

[0046]

[0047] in

[0048] Step 3.2.2, Calculate And for N (p) Perform SVD decomposition to obtain and

[0049] Step 3.2.3, Update C (p+1) 、V (p+1)

[0050]

[0051] Step 3.2.4,

[0052] Among them, Σ (p+1),r Yes Select The first r large eigenvalues ​​form a matrix of rank r

[0053] Step 3.3, μ (p) and L (p+1) Substitution Problem

[0054]

[0055] Solve to get the sparse term M (p+1) ;

[0056] Step 3.4, Update μ (p+1)

[0057]

[0058] where μ max and ρ are fixed values ​​set before the cycle;

[0059] P = P + 1;

[0060] Step 3.5, loop steps 3.2-3.4 until convergence is achieved, output L and M, and use M as useful data after interference removal for imaging to obtain a SAR image with interference removed; the imaging effect is as follows: Figure 3 shown.

[0061] We then use the traditional time-frequency domain notch filtering method to process the SAR image, and the required processing time is about 55 times that of the present invention.

[0062] We use this method to perform imaging tests on another pair of SAR images of Sentinel-1A. The original SAR image is shown in FIG4 . There is a lot of RF interference on the image as a whole. The image processed by the present invention is shown in FIG4 . Figure 5 shown.

[0063] In summary, in this example, the present invention can achieve the effects of suppressing radio frequency interference, accelerating calculation speed, and protecting real echoes in a SAR system.

[0064] Matters not covered by the present invention are known technologies.

[0065] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A semi-parametric fast suppression method for SAR system radio frequency interference, characterized in that: The steps include: Step 1: sparsely decompose the SAR image matrix data S and transform it into solving the optimization problem in, is the raw data of the SAR image, L is the interference in the form of a matrix of low-rank terms, M is the useful signal in the form of a matrix of sparse terms, δ is the allowable noise error, λ is the hyperparameter for adjusting the relationship between the solution results L and M, μ is the penalty parameter, Y1 is the Lagrange multiplier, ‖·‖ * represents the nuclear norm of the matrix, ‖·‖1 represents the l1 norm of the matrix, ‖·‖ F represents the F norm of the matrix; Step 2: divide the optimization problem in step 1 into two sub-optimization problems, namely, solving the optimization problem of the low-rank term L and the optimization problem of the sparse term M; The optimization problem of solving the sparse term M is expressed as: The optimization problem of solving the low-rank term L is expressed as: strank(L) = r; Where r represents the rank of the low-rank term L; Step 3, alternately optimizing and solving the optimization problem of M and the optimization problem of L to obtain the sparse term M and the low-rank term L, outputting the useful signal of the sparse term M to obtain the SAR image after removing interference; In step 3, the optimization problem of M and the optimization problem of L are alternately optimized and solved to obtain the sparse term M and the low-rank term L; specifically, the steps include: Step 3.1, set P = 0, P represents the number of iterations, set C (p) 、V (p) , μ (p) and L (p) The initial value of Step 3.2, M (p) , μ (p) and Substitute into the optimization problem to solve the low-rank term L; The specific steps are as follows: Step 3.2.1, perform QR decomposition on the following disclosure to obtain Q1, Q2, R1 and R2. The specific formula is: in, Step 3.2.2, Calculate And for N (p) Perform SVD decomposition to obtain and Step 3.2.3, Update C (p+1) 、V (p+1) : Step 3.2.4, Among them, Σ (p+1),r Yes Select The first r largest eigenvalues ​​form a matrix of rank r; Step 3.3, μ (p) and L (p+1) Substitution problem: Solve to get the sparse term M (p+1) ; Step 3.4, Update μ (p+1) : Among them, μ max and ρ are fixed values ​​set before the cycle; P = P + 1; Step 3.5, loop steps 3.2-3.4 until convergence is achieved, output L and M, and use M as useful data after interference removal for imaging to obtain an interference-free SAR image.

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