Support agnostic Bayesian recovery of jointly sparse signals

Mudassir Masood, Tareq Y. Al-Naffouri

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

3 Scopus citations

Abstract

A matching pursuit method using a Bayesian approach is introduced for recovering a set of sparse signals with common support from a set of their measurements. This method performs Bayesian estimates of joint-sparse signals even when the distribution of active elements is not known. It utilizes only the a priori statistics of noise and the sparsity rate of the signal, which are estimated without user intervention. The method utilizes a greedy approach to determine the approximate MMSE estimate of the joint-sparse signals. Simulation results demonstrate the superiority of the proposed estimator.
Original languageEnglish (US)
Title of host publication22nd European Signal Processing Conference, EUSIPCO 2014
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1741-1745
Number of pages5
ISBN (Print)9780992862619
StatePublished - Jan 1 2014

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