Distributed compressed sensing for sensor networks using thresholding

Mohammad Golbabaee, Pierre Vandergheynst

Research output: Contribution to journalArticlepeer-review


Distributed compressed sensing is the extension of compressed sampling (CS) to sensor networks. The idea is to design a CS joint decoding scheme at a central decoder (base station) that exploits the inter-sensor correlations, in order to recover the whole observations from very few number of random measurements per node. In this paper, we focus on modeling the correlations and on the design and analysis of efficient joint recovery algorithms. We show, by extending earlier results of Baron et al.,1 that a simple thresholding algorithm can exploit the full diversity offered by all channels to identify a common sparse support using a near optimal number of measurements.
Original languageEnglish
JournalProceedings of SPIE - International Society for Optical Engineering
Publication statusPublished - 1 Aug 2009


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