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Distributed Compressed Sensing for Sensor Networks, Using p-thresholding

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 the base station which exploits the inter-sensor correlations, in order to recover the whole observations from very few number of random measurements per node. Here, the questions are about modeling the correlations, design of the joint recovery algorithms, analysis of those algorithms, the comparison between the performance of the joint and separate decoder and finally determining how optimal they are.
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Submitted on : Tuesday, March 24, 2009 - 11:28:53 AM
Last modification on : Monday, October 2, 2017 - 4:06:02 PM
Long-term archiving on: : Friday, October 12, 2012 - 2:00:51 PM


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  • HAL Id : inria-00369552, version 1



Mohammad Golbabaee, Pierre Vandergheynst. Distributed Compressed Sensing for Sensor Networks, Using p-thresholding. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Inria Rennes - Bretagne Atlantique, Apr 2009, Saint Malo, France. ⟨inria-00369552⟩



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