Sparse reconstruction using distribution agnostic bayesian matching pursuit

Handle URI:
http://hdl.handle.net/10754/563065
Title:
Sparse reconstruction using distribution agnostic bayesian matching pursuit
Authors:
Masood, Mudassir ( 0000-0003-0462-7874 ) ; Al-Naffouri, Tareq Y.
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.
KAUST Department:
Electrical Engineering Program
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Signal Processing
Issue Date:
Nov-2013
DOI:
10.1109/TSP.2013.2278814
ARXIV:
arXiv:1206.4208
Type:
Article
ISSN:
1053587X
Sponsors:
This work was funded in part by a CRG2 grant CRG\_R2\_13\_ALOU\_KAUST\_2 from the Office of Competitive Research (OCRF) at King Abdullah University of Science and Technology (KAUST). The work of T.Y. Al-Naffouri was also supported by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at King Fahd University of Petroleum & Minerals (KFUPM) through Project No. 09-ELE763-04 as part of the National Science, Technology and Innovation Plan.
Additional Links:
http://arxiv.org/abs/arXiv:1206.4208v1
Appears in Collections:
Articles; Electrical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorMasood, Mudassiren
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.date.accessioned2015-08-03T11:34:58Zen
dc.date.available2015-08-03T11:34:58Zen
dc.date.issued2013-11en
dc.identifier.issn1053587Xen
dc.identifier.doi10.1109/TSP.2013.2278814en
dc.identifier.urihttp://hdl.handle.net/10754/563065en
dc.description.abstractA 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.en
dc.description.sponsorshipThis work was funded in part by a CRG2 grant CRG\_R2\_13\_ALOU\_KAUST\_2 from the Office of Competitive Research (OCRF) at King Abdullah University of Science and Technology (KAUST). The work of T.Y. Al-Naffouri was also supported by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at King Fahd University of Petroleum & Minerals (KFUPM) through Project No. 09-ELE763-04 as part of the National Science, Technology and Innovation Plan.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://arxiv.org/abs/arXiv:1206.4208v1en
dc.subjectBasis selectionen
dc.subjectBayesianen
dc.subjectcompressed sensingen
dc.subjectgreedy algorithmen
dc.subjectlinear regressionen
dc.subjectmatching pursuiten
dc.subjectminimum mean-square error (MMSE) estimateen
dc.subjectsparse reconstructionen
dc.titleSparse reconstruction using distribution agnostic bayesian matching pursuiten
dc.typeArticleen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journalIEEE Transactions on Signal Processingen
dc.contributor.institutionDepartment of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabiaen
dc.identifier.arxividarXiv:1206.4208en
kaust.authorMasood, Mudassiren
kaust.authorAl-Naffouri, Tareq Y.en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.