A Matching Pursuit Generalized Approximate Message Passing Algorithm

Yongjie LUO  Qun WAN  Guan GUI  Fumiyuki ADACHI  

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
compressed sensing,  generalized approximate message passing,  matching pursuit,  robust,  

Full Text: PDF(805.4KB)>>
Buy this Article

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.