Low rank and sparse matrix decomposition based multiple targets extraction for forward-looking scanning radar

被引:2
作者
Li, Wenchao [1 ]
Zhang, Wentao [1 ]
Yang, Shirui [1 ]
Huang, Yulin [1 ]
Yang, Jianyu [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu, Peoples R China
关键词
forward-looking imaging; scanning radar; superresolution; target extraction; low-rank and sparse matrix decomposition; SUPERRESOLUTION; MAXIMUM;
D O I
10.1117/1.JRS.15.046504
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Forward-looking scanning radar is capable of obtaining the real beam image of terrain in front of the flight platform, and can be used in military and civilian fields, such as sea surface surveillance, search and rescue, etc. However, it is difficult to extract multiple targets from the real beam image due to its poor azimuth resolution. A multiple-target extraction scheme based on low-rank and sparse matrix decomposition is proposed for forward-looking scanning radar. In the scheme, an image with good azimuth resolution is obtained first by the deconvolution preprocessing, and then it is used to map a patch-image. Second, using the low-rank property of the patch-image and the sparse property of the targets of interest, the target extraction is converted into an optimization problem of low-rank and sparse matrix decomposition. Third, the extraction results are obtained by solving this optimization problem using the alternating direction method of multipliers. Finally, simulation and experiment results are given to verify the effectiveness of the proposed scheme. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)
引用
收藏
页数:15
相关论文
共 33 条
[1]  
[Anonymous], 2012, ROBUST PRINCIPAL COM
[2]   Distributed optimization and statistical learning via the alternating direction method of multipliers [J].
Boyd S. ;
Parikh N. ;
Chu E. ;
Peleato B. ;
Eckstein J. .
Foundations and Trends in Machine Learning, 2010, 3 (01) :1-122
[3]   Robust Principal Component Analysis? [J].
Candes, Emmanuel J. ;
Li, Xiaodong ;
Ma, Yi ;
Wright, John .
JOURNAL OF THE ACM, 2011, 58 (03)
[4]  
Chen CF, 2012, PROC CVPR IEEE, P2618, DOI 10.1109/CVPR.2012.6247981
[5]   Sparse super-resolution imaging for airborne single channel forward-looking radar in expanded beam space via lp regularisation [J].
Chen, Hong Meng ;
Li, Ming ;
Wang, Zeyu ;
Lu, Yunlong ;
Zhang, Peng ;
Wu, Yan .
ELECTRONICS LETTERS, 2015, 51 (11) :863-U48
[6]   机场场面监视雷达目标检测新方法 [J].
陈建军 ;
孙俊 ;
李申 ;
王涛 ;
凌云 ;
于立 ;
张煜婕 ;
李文娟 .
数据采集与处理, 2016, 31 (03) :555-561
[7]   Wavelet detection scheme tor small targets in sea clutter [J].
Davidson, G ;
Griffiths, HD .
ELECTRONICS LETTERS, 2002, 38 (19) :1128-1130
[8]   Max-Mean and Max-Median filters for detection of small-targets [J].
Deshpande, SD ;
Er, MH ;
Ronda, V ;
Chan, P .
SIGNAL AND DATA PROCESSING OF SMALL TARGETS 1999, 1999, 3809 :74-83
[9]   CFAR detection of extended objects in high-resolution SAR images [J].
di Bisceglie, M ;
Galdi, C .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (04) :833-843
[10]   Infrared Patch-Image Model for Small Target Detection in a Single Image [J].
Gao, Chenqiang ;
Meng, Deyu ;
Yang, Yi ;
Wang, Yongtao ;
Zhou, Xiaofang ;
Hauptmann, Alexander G. .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (12) :4996-5009