SPARSE BAYESIAN LEARNING BASED MULTICHANNEL RADAR FORWARD LOOKING SUPERRESOLUTION IMAGING CONSIDERING OFF-GRID ERROR

被引：2
|
作者：
Li, Kefeng [1 ]
Li, Wenchao [1 ]
Chen, Rui [1 ]
Yang, Jianyu [1 ]
机构：
[1] Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China
来源：
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年
基金：
中国国家自然科学基金;
关键词：
Multichannel radar; forward-looking imaging; SBL algorithm; super-resolution;
D O I：
10.1109/IGARSS52108.2023.10282856
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
Multichannel radar has the potential of forward-looking imaging, but its azimuth resolution is usually poor due to the restriction of the platform size. Many superresolution methods have been developed to improve its azimuth resolution. However, these methods always have the problem of grid mismatch. In this paper, sparse Bayesian learning (SBL)-based multichannel radar forward-looking super-resolution imaging considering off-grid error is proposed, and simulation results are illustrated to verify its effectiveness.
引用
收藏
页码：8289 / 8291
页数：3
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