Support agnostic Bayesian matching pursuit for block sparse signals

Mudassir Masood, Tareq Y. Al-Naffouri

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

8 Scopus citations

Abstract

A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and 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 block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.
Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4643-4647
Number of pages5
ISBN (Print)9781479903566
DOIs
StatePublished - May 2013

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