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Proceedings Paper

Distributed compressed sensing for sensor networks using thresholding
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Paper Abstract

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.

Paper Details

Date Published: 4 September 2009
PDF: 6 pages
Proc. SPIE 7446, Wavelets XIII, 74461F (4 September 2009); doi: 10.1117/12.827880
Show Author Affiliations
Mohammad Golbabaee, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
Pierre Vandergheynst, Ecole Polytechnique Fédérale de Lausanne (Switzerland)


Published in SPIE Proceedings Vol. 7446:
Wavelets XIII
Vivek K. Goyal; Manos Papadakis; Dimitri Van De Ville, Editor(s)

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