Adaptive Sparse Recovery by Parametric Weighted L1 Minimization for ISAR Imaging of Uniformly Rotating Targets

被引:66
作者
Rao, Wei [1 ]
Li, Gang [1 ]
Wang, Xiqin [1 ]
Xia, Xiang-Gen [2 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Univ Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA
基金
新加坡国家研究基金会; 中国国家自然科学基金; 美国国家科学基金会;
关键词
Adaptive sparse representation; ISAR imaging; parametric weighted L-1 minimization; SYNTHETIC-APERTURE RADAR; SIGNAL RECOVERY; RECONSTRUCTION;
D O I
10.1109/JSTARS.2012.2215915
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
It has been shown in the literature that, the inverse synthetic aperture radar (ISAR) echo can be seen as sparse and the ISAR imaging can be implemented by sparse recovery approaches. In this paper, we propose a new parametric weighted L-1 minimization algorithm for ISAR imaging based on the parametric sparse representation of ISAR signals. Since the basis matrix used for sparse representation of ISAR signals is determined by the unknown rotation parameter of a moving target, we have to estimate both the ISAR image and basis matrix jointly. The proposed algorithm can adaptively refine the basis matrix to achieve the best sparse representation for the ISAR signals. Finally the high-resolution ISAR image is obtained by solving a weighted L-1 minimization problem. Both numerical and real experiments are implemented to show the effectiveness of the proposed algorithm.
引用
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页码:942 / 952
页数:11
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