Structure based Bayesian sparse reconstruction using non-Gaussian prior

Ahmed A. Quadeer*, Syed Faraz Ahmed, Tareq Y. Al-Naffouri

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

In this paper, we present a fast Bayesian method for sparse signal recovery that makes a collective use of the sparsity information, a priori statistical properties, and the structure involved in the problem to obtain near optimal estimates at very low complexity. Specifically, we utilize the rich structure present in the sensing matrix encountered in many signal processing applications to develop a fast reconstruction algorithm when the statistics of the sparse signal are non-Gaussian or unknown. The proposed method outperforms the widely used convex relaxation approaches as well as greedy matching pursuit techniques all while operating at a much lower complexity.

Original languageEnglish (US)
Title of host publication2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011
Pages277-283
Number of pages7
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011 - Monticello, IL, United States
Duration: Sep 28 2011Sep 30 2011

Other

Other2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011
CountryUnited States
CityMonticello, IL
Period09/28/1109/30/11

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Systems Engineering

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