Distributed Compressed Sensing for Sensor Networks Using Thresholding

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.


Published in:
Proceedings of the SPIE 2009, 7446, 7446-50
Presented at:
Wavelet XIII, SPIE, San Diego, CA, USA, August 2-6, 2009
Year:
2009
Keywords:
Note:
Invited Paper
Laboratories:




 Record created 2009-10-20, last modified 2018-12-03

n/a:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)