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Distributed Compressed Sensing of Jointly Sparse Signals

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Title: Distributed Compressed Sensing of Jointly Sparse Signals
Author: Sarvotham, Shriram; Baron, Dror; Wakin, Michael; Duarte, Marco F.; Baraniuk, Richard G.
Type: Conference Paper
Keywords: joint recovery
Citation: S. Sarvotham, D. Baron, M. Wakin, M. F. Duarte and R. G. Baraniuk,"Distributed Compressed Sensing of Jointly Sparse Signals," in Asilomar Conference on Signals, Systems, and Computers,, pp. 1537-1541.
Abstract: Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we expand our theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- and inter-signal correlation structures. The DCS theory 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 multiple signals from incoherent projections through simultaneous greedy pursuit algorithms. We also characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction.
Date Published: 2005-11-01

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  • ECE Publications [1082 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.