Structure-based bayesian sparse reconstruction

Ahmed Abdul Quadeer, Tareq Y. Al-Naffouri

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.
Original languageEnglish (US)
Pages (from-to)6354-6367
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume60
Issue number12
DOIs
StatePublished - Dec 2012

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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