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

页码：389 / 393

页数：5

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