Computational efficient structural sparse ISAR imaging method based on convolutional ADMM-net

被引：0
作者：
Li R. [1 ]
Zhang S. [1 ]
Liu Y. [1 ]
机构：
[1] College of Electronic Science and Technology, National University of Defense Technology, Changsha
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 01期
关键词：
compressive sensing (CS); deep learning; deep unfolding; inverse synthetic aperture radar (ISAR);
D O I：
10.12305/j.issn.1001-506X.2023.01.08
中图分类号：
学科分类号：
摘要：
Structural sparse inverse synthetic aperture radar (ISAR) imaging is an important approach for situation awareness and object recognition. It can be solved via compressive sensing (CS) methods. At present, many conventional CS algorithms still suffer from low computational efficiency and poor parameter adaptability. In this paper, a structural sparse ISAR imaging method based on convolutional alternating direction method of multipliers network (C-ADMMN) is proposed to overcome those problems. The network is established via deep unfolding methods combined with traditional structural sparse ISAR imaging models. The network only needs approximate 10 layers to achieve the effect of hundreds of iterations in traditional methods through supervised learning. The network achieves higher computing efficiency and has a certain sdaptability to different goals, which is proved on the experiment results based on simulated and measured data. © 2023 Chinese Institute of Electronics. All rights reserved.
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页码：56 / 70
页数：14
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