Sparse reconstruction using distribution agnostic bayesian matching pursuit

Mudassir Masood, Tareq Y. Al-Naffouri

Research output: Contribution to journalArticlepeer-review

72 Scopus citations

Abstract

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.
Original languageEnglish (US)
Pages (from-to)5298-5309
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume61
Issue number21
DOIs
StatePublished - Nov 2013

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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