A new backtracking-based sparsity adaptive algorithm for distributed compressed sensing

A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing (DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any p...

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Bibliographic Details
Published inJournal of Central South University Vol. 22; no. 10; pp. 3946 - 3956
Main Authors Xu, Yong, Zhang, Yu-jie, Xing, Jing, Li, Hong-wei
Format Journal Article
LanguageEnglish
Published Changsha Central South University 01.10.2015
Subjects
Engineering
Metallic Materials
distributed compressed sensing
sparsiy
backtracking
soft thresholding
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ISSN2095-2899
2227-5223
DOI10.1007/s11771-015-2939-2

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Summary:A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing (DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but the numbers of non-zero (significant) coefficients of signals are not available. Numerical experiments are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing strong DCS algorithms.
ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-015-2939-2