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A joint recovery algorithm for distributed compressed sensing
Author(s) -
Xu Wenbo,
Lin Jiaru,
Niu Kai,
He Zhiqiang
Publication year - 2012
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.2509
Subject(s) - compressed sensing , signal recovery , component (thermodynamics) , joint (building) , computer science , exploit , algorithm , signal (programming language) , sparse approximation , sparse matrix , engineering , architectural engineering , physics , computer security , quantum mechanics , gaussian , thermodynamics , programming language
Distributed compressed sensing exploits the correlation among multiple signals to reduce the number of measurements required for recovery. In this paper, we propose a recovery algorithm for a type of joint sparsity model, where all signals share a common sparse component and each individual signal contains a sparse innovation component. Our approach iteratively removes the information of each component from the measurements and then performs sparse recovery. We provide analytical analysis to verify the advantage of the proposed algorithm over separate recovery, which is also confirmed by simulation results. Copyright © 2012 John Wiley & Sons, Ltd.