Structure-based bayesian sparse reconstruction

Ahmed Abdul Quadeer; Tareq Y. Al-Naffouri

doi:10.1109/TSP.2012.2215029

Structure-based bayesian sparse reconstruction

Ahmed Abdul Quadeer, Tareq Y. Al-Naffouri

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

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.

Original language	English (US)
Pages (from-to)	6354-6367
Number of pages	14
Journal	IEEE Transactions on Signal Processing
Volume	60
Issue number	12
DOIs	https://doi.org/10.1109/TSP.2012.2215029
State	Published - Dec 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

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10.1109/TSP.2012.2215029

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title = "Structure-based bayesian sparse reconstruction",

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. {\textcopyright} 1991-2012 IEEE.",

author = "Quadeer, {Ahmed Abdul} and Al-Naffouri, {Tareq Y.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: 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.",

year = "2012",

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doi = "10.1109/TSP.2012.2215029",

language = "English (US)",

volume = "60",

pages = "6354--6367",

journal = "IEEE Transactions on Signal Processing",

issn = "1053-587X",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/562446

UR - http://arxiv.org/abs/arXiv:1207.3847v1

UR - http://www.scopus.com/inward/record.url?scp=84870508852&partnerID=8YFLogxK

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DO - 10.1109/TSP.2012.2215029

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EP - 6367

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 12

ER -

Structure-based bayesian sparse reconstruction

Abstract

Bibliographical note

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