基于迭代加权L2/L1范数块稀疏信号重构的ISAR成像算法

doi:10.19678/j.issn.1000-3428.0048939

计算机工程 ›› 2018, Vol. 44 ›› Issue (11): 234-238. doi: 10.19678/j.issn.1000-3428.0048939

基于迭代加权L2/L1范数块稀疏信号重构的ISAR成像算法

冯俊杰^1,2,张弓¹

1.南京航空航天大学电子信息工程学院,南京 211106; 2.六盘水师范学院电气工程学院,贵州六盘水 553004

收稿日期:2017-10-13 出版日期:2018-11-15 发布日期:2018-11-15
作者简介:冯俊杰(1983—),男,博士研究生,主研方向为图像信号处理;张弓,教授、博士生导师。
基金资助:
国家自然科学基金(61471191);航空科学基金(20152052026);贵州省科学技术基金(黔科合LH字［2014］7471号);贵州省重点学科项目(ZDXK201535)。

ISAR Imaging Algorithm Based on Iterative Weighted L2/L1 Norm Block Sparse Signal Recovery

FENG Junjie^1,2,ZHANG Gong¹

1.College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China; 2.School of Electrical Engineering,Liupanshui Normal University,Liupanshui,Guizhou 553004,China

Received:2017-10-13 Online:2018-11-15 Published:2018-11-15

摘要/Abstract

摘要：

为实现快速、高分辨率逆合成孔径雷达(ISAR)成像,利用目标的内在块稀疏结构信息,提出一种迭代加权L2/L1范数块稀疏重构ISAR成像算法。构建ISAR稀疏成像模型,将ISAR成像问题转化为稀疏信号重构问题后,在每次迭代中求解用于下次迭代的权值向量解,从而实现高分辨率ISAR成像。实验结果表明,相比BP、OMP、SBL算法,该算法可以改善成像质量,提高重构效率。

关键词: 逆合成孔径雷达, 迭代加权, L2/L1范数, 稀疏信号重构, 稀疏成像

Abstract:

In order to realize fast and high resolution Inverse Synthetic Aperture Radar(ISAR)imaging,an iterative weighted L2/L1 norm block sparse recovery algorithm for ISAR imaging is proposed based on the target’s intrinsic block sparse structure information.After constructing the ISAR sparse imaging model and transforming the ISAR imaging problem into the sparse signal recovery problem,the weighted vector solution for the next iteration is solved in each iteration,and then the high resolution ISAR imaging is realized.Experimental results show that compared with BP,OMP and SBL,the proposed algorithm can improve the image quality and recovery efficiency.

Key words: Inverse Synthetic Aperture Radar(ISAR), iterative weighted, L2/L1 norm, sparse signal recovery, sparse imaging

中图分类号:

TN95

冯俊杰,张弓. 基于迭代加权L2/L1范数块稀疏信号重构的ISAR成像算法[J]. 计算机工程, 2018, 44(11): 234-238.

FENG Junjie,ZHANG Gong. ISAR Imaging Algorithm Based on Iterative Weighted L2/L1 Norm Block Sparse Signal Recovery[J]. Computer Engineering, 2018, 44(11): 234-238.

http://www.ecice06.com/CN/Y2018/V44/I11/234

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[1]	冯俊杰,张弓. 参数迭代最小化稀疏信号重构ISAR成像算法[J]. 计算机工程, 2018, 44(10): 228-234.
[2]	袁静. 基于压缩感知的核磁共振成像重构算法[J]. 计算机工程, 2015, 41(10): 270-274.

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