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Optimized compressive sensing stepped frequency SAR image restoration and reconstruction method

A technology of compressed sensing and step frequency, applied in the field of radar imaging, can solve the problems of high computational complexity, achieve the effect of improving computational efficiency and reducing storage requirements

Inactive Publication Date: 2018-12-14
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

In order to further improve the performance, Fu Ning et al. proposed a correlation coefficient-based orthogonal matching pursuit algorithm (Amodified orthogonal matching algorithm using correlation coefficient for compressed sensing. I2MTC, May 10-12, 2011: 1-5) using the perception matrix and The correlation coefficient between the measurement vectors replaces the support domain of the inner product estimation signal, although the probability of successful reconstruction is improved, but the calculation amount is about 3 times that of the classic OMP, and the calculation complexity is too high

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[0057] Step 1: According to the above parameter settings, the complete echo data is obtained through simulation, and the compressed sampling echo data matrix Y is obtained according to the 4 times compression ratio, and its size is 64×64. The sparsity of the distribution of target scattering rate K=46, and the energy threshold ε of the residual signal. sensor matrix in Represents the Kronecker product, constructing Θ d , whose size is 64×128, Θ d , whose size is 64×128.

[0058] The specific theory is as follows: the compression measurement model at the lth viewing angle is Among them, l=1,2,...,L, corresponding to the viewing angle order of sampling, Represents the range-directed sampling matrix, Ψ l Represents the sparse dictionary at the lth view. Let y=[y 1 ,y 2 ,...,y l ], Φ=[Φ 1 ,Φ 2 ,…,Φ l ], Ψ=[Ψ 1 ,Ψ 2 ,…,Ψ l ], then the joint compressed sampling model is y=ΦΨσ=Θσ, when the matrix Θ satisfies the RIP criterion or the incoherence condition, σ can be...

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Abstract

The invention provides an optimized compressive sensing stepped frequency SAR image restoration and reconstruction method and relates to the field of radar imaging technology. According to the method,after an operation corresponding relation of Kronecker products is utilized to calculate column vectors, column sets in a sensing matrix are updated; then a least square method is adopted to estimatea target signal in a current column set phi, and residual signal vectors are updated till a termination condition is met; and assignment is performed on target scene scattering distribution accordingto an established index position, and then a scattering rate distribution two-dimensional target image can be obtained. Through the method, according to a sparse representation model of a stepped frequency radar echo signal, two-dimensional separability of the sensing matrix and a sparse dictionary is utilized, the method of finding columns in highest correlativity from the sensing matrix is optimized, the demand on storage quantity is lowered, and calculation efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of radar imaging, in particular to a SAR imaging method based on compressed sensing. Background technique [0002] High-resolution radar imaging systems are widely used in military and civilian fields, but limited by the Nyquist sampling theorem, traditional radars face high sampling rates, large data volumes, and rapid The challenge of dealing with difficult and other issues. Specifically, there are mainly the limitations of A / D conversion technology in wideband radar, the impact of matched filtering on resolution, the constraints of uncertainty principle on time-frequency resolution, the unsoundness of radar data itself and the excessive data storage and transmission volume, etc. question. The scattering characteristics of radar targets in the high-frequency region can be described by a few important scattering centers, and the echoes are potentially sparse. Therefore, compressed sensing is very attracti...

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Application Information

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IPC IPC(8): G01S13/90
CPCG01S13/904G01S13/9004
Inventor 王保平韩昭旋马健钧方阳张研
Owner NORTHWESTERN POLYTECHNICAL UNIV
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