JOINTLY SPARSE VECTOR RECOVERY VIA REWEIGHTED l1 MINIMIZATION

被引：0

作者：

Wei, Mu-Hsin ^{[1
]}

Scott, Waymond R., Jr. ^{[1
]}

McClellan, James H. ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2012年

关键词：

Jointly sparse; multiple-measurement vector; basis pursuit; iterative reweighting; ELECTROMAGNETIC INDUCTION RESPONSES; MULTIPLE-MEASUREMENT VECTORS; LINEAR INVERSE PROBLEMS; DISCRETE SPECTRUM; ALGORITHMS; APPROXIMATION; RELAXATIONS; PURSUIT;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

An iterative reweighted algorithm is proposed for the recovery of jointly sparse vectors from multiple-measurement vectors (MMV). The proposed MMV algorithm is an extension of the iterative reweighted l(1) algorithm for single measurement problems. The proposed algorithm (M-IRL1) is demonstrated to outperform non-reweighted MMV algorithms under noiseless measurements. A regularization of the M-IRL1 algorithm is also proposed to accommodate noise. The ability to robustly handle noise is demonstrated through an electromagnetic induction application.

引用

页码：3929 / 3932

页数：4

共 14 条

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