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Conditional prior based lmmse estimation of sparse signals
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2638-6047
2013 (English)In: 2013 Proceedings of the 21st European Signal Processing Conference (EUSIPCO), IEEE conference proceedings, 2013, 6811629- p.Conference paper, Published paper (Refereed)
Abstract [en]

We derive a linear minimum mean square error estimator for sparse vector estimation from an underdetermined set of linear equations. The derivation of the estimator uses a prior distribution conditioned on the support set of the underlying sparse vector. The estimator is used in the architecture of the standard orthogonal matching pursuit algorithm to achieve a better performance.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 6811629- p.
Series
European Signal Processing Conference, ISSN 2219-5491
Keyword [en]
Bayesian estimation, minimum mean square error estimation, sparse estimation, compressed sensing.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-129192ISI: 000341754500242Scopus ID: 2-s2.0-84901374395ISBN: 978-099286260-2 (print)OAI: oai:DiVA.org:kth-129192DiVA: diva2:651926
Conference
2013 21st European Signal Processing Conference, EUSIPCO 2013; Marrakech; Morocco; 9 September 2013 through 13 September 2013
Note

QC 20140619

Available from: 2013-09-27 Created: 2013-09-23 Last updated: 2014-10-21Bibliographically approved

Open Access in DiVA

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