Bi-ISAR imaging based on weighted l1 norm optimization algorithm

被引：0
|
作者：
Xue D. [1 ]
Zhu X. [1 ]
Hu W. [1 ]
Guo B. [1 ]
Zeng H. [1 ]
机构：
[1] Shijiazhuang Campus of the Army Engineering University, Shijiazhuang
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 04期
关键词：
Bistatic inverse synthetic aperture radar (ISAR); Compressive sensing; Optimization theory; Sparse apertures; Weighted l[!sub]1[!/sub] norm;
D O I：
10.12305/j.issn.1001-506X.2021.04.11
中图分类号：
学科分类号：
摘要：
To solve the problem of poor reconstruction quality in bistatic inverse synthetic aperture radar (ISAR) sparse aperture imaging under low signal-to-noise ratio conditions, a high resolution imaging algorithm based on weighted l1 norm optimization is proposed. First, assuming that the image pixels are sparsely distributed, the Bayesian criterion and the maximum a posteriori probability estimation are used to transform the bistatic ISAR sparse aperture imaging problem into a weighted l1 norm constraint problem, and the imaging model is established. Second, the Cauchy-Newton algorithm is used to solve the weighted l1 norm constrained optimization problem and obtain the target image reconstruction. Because the pixels are assumed to be independent and non-uniformly distributed, the energy aggregation and structural characteristics of the target are better utilized in the way of weighting, which improves the imaging quality. Finally, simulation experiments verify the effectiveness and superiority of the algorithm. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：944 / 953
页数：9
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