Low Rank Plus Sparse Decomposition of Synthetic Aperture Radar Data For Maritime Surveillance

被引：0
|
作者：
Biondi, Filippo [1 ,2 ,3 ]
机构：
[1] Nation Res Council ISSIA CNR, Bari, Italy
[2] Italian Space Agcy, Geodesy Space Ctr Matera, Rome, Italy
[3] Italian Minist Def, Joint Satellite Remote Sensing Ctr, Rome, Italy
来源：
2016 4TH INTERNATIONAL WORKSHOP ON COMPRESSED SENSING THEORY AND ITS APPLICATIONS TO RADAR, SONAR AND REMOTE SENSING (COSERA) | 2016年
关键词：
Synthetic Aperture Radar; Multi Chromatic Analysis; Compressed Sensing; Low-rank Matrix Completion; Sparsity; Dynamic SAR; SAR;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Synthetic Aperture Radar (SAR) systems produce a tremendous amount of redundant data if persistent radar surveillance of a specific area is implemented. This paper performs an efficient data reduction extrapolating maritime targets in motion from background subtraction. The technique is based on Robust Principal Component Analysis (RPCA). The algorithm is implemented by Convex Programming (CP). This Low Rank and Sparse Decomposition (LRSD) activity permits the separation of sparse objects of interest, with a stationary low-rank background. RPCA applied to SAR surveillance permits the saving of a large amount of data. Dynamic SAR is procured by Multi Chromatic Analysis (MCA) of Native (RAW)(1) satellite data.
引用
收藏
页码：75 / 79
页数：5
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