KNOWLEDGE-ENHANCED MATCHING PURSUIT

被引：0

作者：

Chi, Yuejie ^{[1
]}

Calderbank, Robert ^{[2
]}

机构：

[1] Ohio State Univ, Elect & Comp Engn, Columbus, OH 43210 USA

[2] Duke Univ, Elect & Comp Engn, Durham, NC 27706 USA

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2013年

关键词：

matching pursuit; sparsity; support recovery; oblique projection;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Compressive Sensing is possible when the sensing matrix acts as a near isometry on signals of interest that can be sparsely or compressively represented. The attraction of greedy algorithms such as Orthogonal Matching Pursuit is their simplicity. However they fail to take advantage of both the structure of the sensing matrix and any prior information about the sparse signal. This paper introduces an oblique projector to matching pursuit algorithms to enhance detection of a component that is present in the signal by reducing interference from other candidate components based on prior information about the signal as well as the structure of the sensing matrix. Numerical examples demonstrate that performance as a function of SNR is superior to conventional matching pursuit.

引用

页码：6576 / 6580

页数：5

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