基于特征增强的非均匀采样SAR三维稀疏成像

doi:10.12305/j.issn.1001-506X.2021.04.06

Abstract

Abstract:

For non-uniform sampling data, the local data interpolation in the existing three-dimensional (3D) sparse imaging methods will bring errors, and the imaging result of distributed targets is poor. To solve these problems, firstly, the imaging problem is directly modeled as a joint sparse reconstruction problem in 3D space, and the dictionary reduction is carried out by selecting the candidate scattering centers; secondly, the 3D feature enhancement constraints are added to the reduced model to establish the relationship between adjacent scattering centers in 3D space; finally, an efficient model solving algorithm is proposed by combining the Gaussian iterative method and the optimized signal processing scheme. The experimental results show that, compared with other imaging methods, the proposed method has a strong ability to suppress side lobe, whose imaging results are with high resolution and high precision, and performs well on distributed targets.

Key words: synthetic aperture radar, non-uniform sampling, three-dimensional (3D) imaging, sparse reconstruction, feature enhancement

CLC Number:

TN958.97

Dou SUN, Shiqi XING, Haifeng GAO, Bo PANG, Yongzhen LI, Xuesong WANG. 3D sparse imaging for non-uniformly sampled SAR based on feature enhancement[J]. Systems Engineering and Electronics, 2021, 43(4): 901-910.

Figures/Tables 14

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References 30

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方法	3D-NUFFT	Austin	本文
时间/s	0.72	1.07	1.68

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3D sparse imaging for non-uniformly sampled SAR based on feature enhancement

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