Distributed compressed sensing of jointly sparse signals

Marco F. Duarte, Shriram Sarvotham, Dror Baron, Michael B. Wakin, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

479 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationConference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers
Number of pages5
StatePublished - Dec 1 2005
Event39th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Oct 28 2005Nov 1 2005


Other39th Asilomar Conference on Signals, Systems and Computers
Country/TerritoryUnited States
CityPacific Grove, CA

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


Dive into the research topics of 'Distributed compressed sensing of jointly sparse signals'. Together they form a unique fingerprint.

Cite this