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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

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2018 Impact Factor: 1.129

Front. Comput. Sci.    2014, Vol. 8 Issue (6) : 893-904    https://doi.org/10.1007/s11704-014-3461-7
RESEARCH ARTICLE
A survey on distributed compressed sensing: theory and applications
Hongpeng YIN1,2,*(),Jinxing LI1,Yi CHAI1,3,Simon X. YANG4
1. College of Automation, Chongqing University, Chongqing 400030, China
2. Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing 400030, China
3. State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing 400030, China
4. School of Engineering, University of Guelph, Guelph N1G 2W1, Canada
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Abstract

The compressed sensing (CS) theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing (DCS) is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS’s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.

Keywords compressed sensing      distributed compressed sensing      sparse representation      measurement matrix      joint reconstruction      joint sparsity model     
Corresponding Author(s): Hongpeng YIN   
Issue Date: 27 November 2014
 Cite this article:   
Hongpeng YIN,Jinxing LI,Yi CHAI, et al. A survey on distributed compressed sensing: theory and applications[J]. Front. Comput. Sci., 2014, 8(6): 893-904.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-014-3461-7
https://academic.hep.com.cn/fcs/EN/Y2014/V8/I6/893
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