A survey on distributed compressed sensing: theory and applications |
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| Authors: | Hongpeng YIN Jinxing LI Yi CHAI Simon X. YANG |
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| Affiliation: | 1. College of Automation, Chongqing University, Chongqing 400030, China2. Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing 400030, China3. State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing 400030, China4. 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. |
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| Keywords: | compressed sensing distributed compressed sensing sparse representation measurement matrix joint reconstruction joint sparsity model |
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