Distributed Compressed Sensing of Jointly Sparse Signals

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameAsilomar Conference on Signals, Systems, and Computersen_US
dc.citation.firstpage1537en_US
dc.citation.lastpage1541en_US
dc.citation.locationPacific Grove, CAen_US
dc.contributor.authorSarvotham, Shriramen_US
dc.contributor.authorBaron, Droren_US
dc.contributor.authorWakin, Michaelen_US
dc.contributor.authorDuarte, Marco F.en_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:43:06Zen_US
dc.date.available2007-10-31T00:43:06Zen_US
dc.date.issued2005-11-01en_US
dc.date.modified2006-07-19en_US
dc.date.note2006-06-06en_US
dc.date.submitted2005-11-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractCompressed 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.en_US
dc.description.sponsorshipAir Force Office of Scientific Researchen_US
dc.description.sponsorshipOffice of Naval Researchen_US
dc.description.sponsorshipNational Science Foundationen_US
dc.identifier.citationS. Sarvotham, D. Baron, M. Wakin, M. F. Duarte and R. G. Baraniuk, "Distributed Compressed Sensing of Jointly Sparse Signals," 2005.en_US
dc.identifier.doihttp://dx.doi.org/10.1109/ACSSC.2005.1600024en_US
dc.identifier.urihttps://hdl.handle.net/1911/19865en_US
dc.language.isoengen_US
dc.subjectjoint recoveryen_US
dc.subject.keywordjoint recoveryen_US
dc.subject.otherDSP for Communicationsen_US
dc.titleDistributed Compressed Sensing of Jointly Sparse Signalsen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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