ONLINE SPARSE SUPER-RESOLUTION METHOD FOR RADAR FORWARD-LOOKING IMAGING USING MAJORIZE-MINIMIZATION

被引:0
|
作者
Yin, Xichen [1 ]
Huang, Yulin
Zhang, Yongchao
Zhang, Yongwei
Tuo, Xingyu
Zhang, Yin
Yang, Jianyu
机构
[1] Univ Elect Sci & Technol China Chengdu, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China
基金
中国国家自然科学基金;
关键词
Scanning radar; super-resolution imaging; online reconstruction; majorize-minimization; RESOLUTION;
D O I
10.1109/IGARSS52108.2023.10281605
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Recently, super-resolution techniques have been widely used in real aperture radar super-resolution imaging. And the majorize-minimization(MM) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this paper, we strive to alleviate this problem, deriving an online MM algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The proposed method is a regularized extension of the current MM implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current MM algorithm. Our experimental assessment, conducted using simulated data, demonstrates the advantage of the online MM(OMM) algorithm in the task of sparse reconstruction for scanning radar.
引用
收藏
页码:7129 / 7132
页数:4
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