Distributed compressed sensing for sensor networks using thresholding

Mohammad Golbabaee; Pierre Vandergheynst

doi:10.1117/12.827880

4 September 2009 Distributed compressed sensing for sensor networks using thresholding

Mohammad Golbabaee, Pierre Vandergheynst

Proceedings Volume 7446, Wavelets XIII; 74461F (2009) https://doi.org/10.1117/12.827880
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

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.

Citation Download Citation

Mohammad Golbabaee and Pierre Vandergheynst "Distributed compressed sensing for sensor networks using thresholding", Proc. SPIE 7446, Wavelets XIII, 74461F (4 September 2009); https://doi.org/10.1117/12.827880

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