Distributed Compressed Sensing of Jointly Sparse Signals
Top Cited Papers
- 18 July 2006
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10586393,p. 1537-1541
- https://doi.org/10.1109/acssc.2005.1600024
Abstract
Compressed sensing is an emerging field based on the revelation that a small collection of linear projecti ons of a sparse signal contains enough information for recon- struction. In this paper we expand our theory for distributed compressed sensing (DCS) that enables new distributed cod- ing algorithms for multi-signal ensembles that exploit both intra- and inter-signal correlation structures. The DCS the- ory rests on a new concept that we term the joint sparsity of a signal ensemble. We present a second new model for jointly sparse signals that allows for joint recovery of multi- ple signals from incoherent projections through simultane- ous greedy pursuit algorithms. We also characterize theo- retically and empirically the number of measurements per sensor required for accurate reconstruction. I. I NTRODUCTIONKeywords
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