A Matching Pursuit Generalized Approximate Message Passing Algorithm

Yongjie LUO  Qun WAN  Guan GUI  Fumiyuki ADACHI  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E98-A   No.12   pp.2723-2727
Publication Date: 2015/12/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E98.A.2723
Type of Manuscript: LETTER
Category: Numerical Analysis and Optimization
Keyword: 
compressed sensing,  generalized approximate message passing,  matching pursuit,  robust,  

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Summary: 
This paper proposes a novel matching pursuit generalized approximate message passing (MPGAMP) algorithm which explores the support of sparse representation coefficients step by step, and estimates the mean and variance of non-zero elements at each step based on a generalized-approximate-message-passing-like scheme. In contrast to the classic message passing based algorithms and matching pursuit based algorithms, our proposed algorithm saves a lot of intermediate process memory, and does not calculate the inverse matrix. Numerical experiments show that MPGAMP algorithm can recover a sparse signal from compressed sensing measurements very well, and maintain good performance even for non-zero mean projection matrix and strong correlated projection matrix.