Distributed Compressed Sensing for Sensor Networks Using Thresholding

Mohammad Golbabaee, Pierre Vandergheynst

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

3 Citations (SciVal)


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
Title of host publicationWavelet XIII, SPIE Optical Engineering + Applications
EditorsVivek K. Goyal
PublisherCurran Associates, Inc.
ISBN (Print)9780819477361
Publication statusPublished - 2009
EventSPIE Optical Engineering + Applications, 2009 - San Diego, California, USA United States
Duration: 2 Aug 20094 Aug 2009

Publication series

NameSPIE - International Society for Optics and Photonics


ConferenceSPIE Optical Engineering + Applications, 2009
Country/TerritoryUSA United States


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