Iteratively Reweighted Least Squares for Block-sparse Recovery

被引：0

作者：

Li, Shuang ^{[1
]}

Li, Qiuwei ^{[1
]}

Li, Gang ^{[1
]}

He, Xiongxiong ^{[1
]}

Chang, Liping ^{[1
]}

机构：

[1] Zhejiang Univ Technol, Coll Informat Engn, Zhejiang Key Lab Signal Proc, Hangzhou 310023, Zhejiang, Peoples R China

来源：

PROCEEDINGS OF THE 2014 9TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA) | 2014年

关键词：

Compressive sensing; underdetermined systems of linear equations; nonconvex optimization; block sparse signal reconstruction; BIRLS; SIGNALS;

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：

The compressive sensing (CS) theory has shown that sparse signals can be reconstructed exactly from much fewer measurements than traditionally believed. What's more, using l(p)-norm minimization with p < 1 can do so with much fewer measurements than with p = 1. In this paper, a novel algorithm is proposed for computing local minima of the nonconvex problem in the block-sparse system. A series of experiments are presented to show the remarkable performance of our proposed algorithm in block sparse signal recovery, and compare the recovery ability of this algorithm with the IRLS and BOMP algorithm.

引用

页码：1061 / 1066

页数：6

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