Distributed Compressed Sensing via Generalized Approximate Message Passing for Jointly Sparse Signals

Jingjing SI  Yinbo CHENG  Kai LIU  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E102-A   No.4   pp.702-707
Publication Date: 2019/04/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E102.A.702
Type of Manuscript: LETTER
Category: Image
Keyword: 
distributed compressed sensing,  generalized approximate message passing,  joint recovery,  joint sparsity model,  

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Summary: 
Generalized approximate message passing (GAMP) is introduced into distributed compressed sensing (DCS) to reconstruct jointly sparse signals under the mixed support-set model. A GAMP algorithm with known support-set is presented and the matching pursuit generalized approximate message passing (MPGAMP) algorithm is modified. Then, a new joint recovery algorithm, referred to as the joint MPGAMP algorithm, is proposed. It sets up the jointly shared support-set of the signal ensemble with the support exploration ability of matching pursuit and recovers the signals' amplitudes on the support-set with the good reconstruction performance of GAMP. Numerical investigation shows that the joint MPGAMP algorithm provides performance improvements in DCS reconstruction compared to joint orthogonal matching pursuit, joint look ahead orthogonal matching pursuit and regular MPGAMP.