FAST MAJORIZE-MINIMIZATION BASED SUPER-RESOLUTION ALGORITHM FOR RADAR FORWARD-LOOKING IMAGING

被引：0
|
作者：
Yin, Xichen [1 ]
Liu, Lin [1 ]
Huang, Yulin [1 ]
Feng, Mengxi [1 ]
Zhang, Yin [1 ]
Yang, Jianyu [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China
来源：
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年
基金：
中国国家自然科学基金;
关键词：
superresolution imaging; majorize-minimization; fast matrix inversion; RECOGNITION;
D O I：
10.1109/IGARSS46834.2022.9883171
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 superresolution imaging. In this paper, we propose a fast sparse superresolution algorithm which is based on majorize-minimization(MM) method to realize fast superresolution imaging of sparse targets in radar forward-looking area. First, we establish a model of rader forwardlooking imaging and analyze the echo signal. Second, we use the majorize-minimization (MM) method to obtain the real target distribution. Due to the expensive computational cost of MM algorithm, we proposed an fast matrix inversion approach which is based on divide and conquer strategy. The superior performance of the proposed method is verified by simulations.
引用
收藏
页码：2055 / 2058
页数：4
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