• 学术论文 •

### 任意稀疏结构的多量测向量快速稀疏重构算法研究

1. 1. 空军预警学院研究生队, 湖北武汉 430019;
2. 空军预警学院空天预警装备系, 湖北武汉 430019
• 收稿日期:2014-01-03 修回日期:2014-07-10 出版日期:2015-04-25
• 作者简介:
• 李少东 男,1987年出生于河北保定.空军预警学院博士生.主要研究方向为压缩感知在ISAR中的应用、雷达成像.E-mail:liying198798@126.com;陈文峰 男,1989年出生于新疆伊犁.空军预警学院硕士生.主要研究方向为雷达成像、压缩感知.E-mail:chenwf925@163.com;杨军 男,1973年出生于云南大理.空军预警学院副教授、硕士生导师.主要研究方向为雷达系统、雷达信号处理与检测理、SAR/ISAR成像等.马晓岩 男,1962年出生于湖北赤壁.空军预警学院教授、博士生导师,主要研究方向为雷达系统、雷达信号处理与检测理、现代信号处理及其应用.
• 基金资助:
• 军队重点项目

### Study on the Fast Sparse Recovery Algorithm via Multiple Measurement Vectors of Arbitrary Sparse Structure

LI Shao-dong1, CHEN Wen-feng1, YANG Jun2, MA Xiao-yan2

1. 1. Department of Graduate Management, Air Force Early Warning Academy, Wuhan, Hubei 430019, China;
2. Department of Air/Space Early Warning Equipment, Air Force Early Warning Academy, Wuhan, Hubei 430019, China
• Received:2014-01-03 Revised:2014-07-10 Online:2015-04-25 Published:2015-04-25

Abstract:

The traditional Sparse Recovery (SR) algorithms are unsuitable for signal reconstruction of Multiple Measurement Vectors (MMV) for the following two reasons,one is the high computing burden,and the other is that the presented algorithms are not used to the case when MMV are arbitrary sparse structure.To solve the problems,a novel fast sparse recovery algorithm is proposed.Firstly,the Matrix Smoothed L0-norm (MSL0) algorithm is adopted to reconstruct the MMV of arbitrary sparse structure and estimate the initial support.Secondly,using the relationship between the sparse level and measurement number,the pre-selection support is obtained from choosing the initial support.Thirdly,the final support is gotten with Bayesian Group Testing (BGT) method.And finally,the MMV is reconstructed precisely via the final support.The proposed algorithm makes full use of high efficiency of the MSL0 and redundancy support elimination ability of the BGT.The algorithm can not only reconstruct MMV of arbitrary sparse structure more efficiently,but also has higher reconstructed accuracy and better robustness.ISAR imaging experiments based on real data show the validity of the proposed algorithm.