Structure-based bayesian sparse reconstruction

Handle URI:
http://hdl.handle.net/10754/562446
Title:
Structure-based bayesian sparse reconstruction
Authors:
Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.
Abstract:
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.
KAUST Department:
Electrical Engineering Program
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Signal Processing
Issue Date:
Dec-2012
DOI:
10.1109/TSP.2012.2215029
ARXIV:
arXiv:1207.3847
Type:
Article
ISSN:
1053587X
Sponsors:
Manuscript received April 24, 2012; revised July 23, 2012; accepted July 30, 2012. Date of publication August 23, 2012; date of current version November 20, 2012. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Z. Jane Wang. This work was partially supported by SABIC through an internally funded project from DSR, KFUPM (Project No. SB101006) and partially by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at KFUPM (Project No. 09-ELE763-04) as part of the National Science, Technology and Innovation Plan. The work of T. Y. Al-Naffouri was also supported by the Fullbright Scholar Program.
Additional Links:
http://arxiv.org/abs/arXiv:1207.3847v1
Appears in Collections:
Articles; Electrical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorQuadeer, Ahmed Abdulen
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.date.accessioned2015-08-03T10:38:29Zen
dc.date.available2015-08-03T10:38:29Zen
dc.date.issued2012-12en
dc.identifier.issn1053587Xen
dc.identifier.doi10.1109/TSP.2012.2215029en
dc.identifier.urihttp://hdl.handle.net/10754/562446en
dc.description.abstractSparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.en
dc.description.sponsorshipManuscript received April 24, 2012; revised July 23, 2012; accepted July 30, 2012. Date of publication August 23, 2012; date of current version November 20, 2012. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Z. Jane Wang. This work was partially supported by SABIC through an internally funded project from DSR, KFUPM (Project No. SB101006) and partially by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at KFUPM (Project No. 09-ELE763-04) as part of the National Science, Technology and Innovation Plan. The work of T. Y. Al-Naffouri was also supported by the Fullbright Scholar Program.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://arxiv.org/abs/arXiv:1207.3847v1en
dc.subjectBayesian methodsen
dc.subjectcompressed sensingen
dc.subjectcompressive samplingen
dc.subjectsignal recoveryen
dc.subjectsparse signal reconstructionen
dc.titleStructure-based bayesian sparse reconstructionen
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.contributor.institutionDepartment of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kongen
dc.identifier.arxividarXiv:1207.3847en
kaust.authorAl-Naffouri, Tareq Y.en
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