A Self-adaptive Proximal Point Algorithm for Signal Reconstruction in Compressive Sensing

被引：0
|
作者：
Huai, Kaizhan [1 ]
Li, Yejun [2 ]
Ni, Mingfang [1 ]
Yu, Zhanke [1 ]
Wang, Xiaoguo [1 ]
机构：
[1] PLA Univ Sci & Technol, Inst Commun Engn, Nanjing, Jiangsu, Peoples R China
[2] Xian Commun Inst, Xian, Shaanxi, Peoples R China
来源：
2016 IEEE INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP) | 2016年
关键词：
compressive sensing; signal reconstruction; proximal point algorithm; self-adaptive; PURSUIT;
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：
Compressive sensing (CS) is a new framework for simulations sensing and compressive. How to reconstruct a sparse signal from limited measurements is the key problem in CS. For solving the reconstruction problem of a sparse signal, we proposed a self-adaptive proximal point algorithm (PPA). This algorithm can handle the sparse signal reconstruction by solving a substituted problem-l(1) problem. At last, the numerical results shows that the proposed method is more effective compared with the compressive sampling matching pursuit (CoSaMP).
引用
收藏
页码：389 / 393
页数：5
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