Distribution agnostic structured sparsity recovery algorithms

Tareq Y. Al-Naffouri, Mudassir Masood

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

6 Scopus citations


We present an algorithm and its variants for sparse signal recovery from a small number of its measurements in a distribution agnostic manner. The proposed algorithm finds Bayesian estimate of a sparse signal to be recovered and at the same time is indifferent to the actual distribution of its non-zero elements. Termed Support Agnostic Bayesian Matching Pursuit (SABMP), the algorithm also has the capability of refining the estimates of signal and required parameters in the absence of the exact parameter values. The inherent feature of the algorithm of being agnostic to the distribution of the data grants it the flexibility to adapt itself to several related problems. Specifically, we present two important extensions to this algorithm. One extension handles the problem of recovering sparse signals having block structures while the other handles multiple measurement vectors to jointly estimate the related unknown signals. We conduct extensive experiments to show that SABMP and its variants have superior performance to most of the state-of-the-art algorithms and that too at low-computational expense. © 2013 IEEE.
Original languageEnglish (US)
Title of host publication2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)9781467355407
StatePublished - May 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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