Structure based Bayesian sparse reconstruction using non-Gaussian prior

Ahmed A. Quadeer; Syed Faraz Ahmed; Tareq Y. Al-Naffouri

doi:10.1109/Allerton.2011.6120179

Structure based Bayesian sparse reconstruction using non-Gaussian prior

Ahmed A. Quadeer^*, Syed Faraz Ahmed, Tareq Y. Al-Naffouri

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

In this paper, we present a fast Bayesian method for sparse signal recovery that makes a collective use of the sparsity information, a priori statistical properties, and the structure involved in the problem to obtain near optimal estimates at very low complexity. Specifically, we utilize the rich structure present in the sensing matrix encountered in many signal processing applications to develop a fast reconstruction algorithm when the statistics of the sparse signal are non-Gaussian or unknown. The proposed method outperforms the widely used convex relaxation approaches as well as greedy matching pursuit techniques all while operating at a much lower complexity.

Original language	English (US)
Title of host publication	2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011
Pages	277-283
Number of pages	7
DOIs	https://doi.org/10.1109/Allerton.2011.6120179
State	Published - 2011
Externally published	Yes
Event	2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011 - Monticello, IL, United States Duration: Sep 28 2011 → Sep 30 2011

Other

Other	2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011
Country/Territory	United States
City	Monticello, IL
Period	09/28/11 → 09/30/11

ASJC Scopus subject areas

Computer Networks and Communications
Control and Systems Engineering

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10.1109/Allerton.2011.6120179

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title = "Structure based Bayesian sparse reconstruction using non-Gaussian prior",

abstract = "In this paper, we present a fast Bayesian method for sparse signal recovery that makes a collective use of the sparsity information, a priori statistical properties, and the structure involved in the problem to obtain near optimal estimates at very low complexity. Specifically, we utilize the rich structure present in the sensing matrix encountered in many signal processing applications to develop a fast reconstruction algorithm when the statistics of the sparse signal are non-Gaussian or unknown. The proposed method outperforms the widely used convex relaxation approaches as well as greedy matching pursuit techniques all while operating at a much lower complexity.",

author = "Quadeer, {Ahmed A.} and Ahmed, {Syed Faraz} and Al-Naffouri, {Tareq Y.}",

year = "2011",

doi = "10.1109/Allerton.2011.6120179",

language = "English (US)",

isbn = "9781457718168",

pages = "277--283",

booktitle = "2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011",

note = "2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011 ; Conference date: 28-09-2011 Through 30-09-2011",

}

Quadeer, AA, Ahmed, SF & Al-Naffouri, TY 2011, Structure based Bayesian sparse reconstruction using non-Gaussian prior. in 2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011., 6120179, pp. 277-283, 2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011, Monticello, IL, United States, 09/28/11. https://doi.org/10.1109/Allerton.2011.6120179

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AU - Ahmed, Syed Faraz

AU - Al-Naffouri, Tareq Y.

PY - 2011

Y1 - 2011

N2 - In this paper, we present a fast Bayesian method for sparse signal recovery that makes a collective use of the sparsity information, a priori statistical properties, and the structure involved in the problem to obtain near optimal estimates at very low complexity. Specifically, we utilize the rich structure present in the sensing matrix encountered in many signal processing applications to develop a fast reconstruction algorithm when the statistics of the sparse signal are non-Gaussian or unknown. The proposed method outperforms the widely used convex relaxation approaches as well as greedy matching pursuit techniques all while operating at a much lower complexity.

AB - In this paper, we present a fast Bayesian method for sparse signal recovery that makes a collective use of the sparsity information, a priori statistical properties, and the structure involved in the problem to obtain near optimal estimates at very low complexity. Specifically, we utilize the rich structure present in the sensing matrix encountered in many signal processing applications to develop a fast reconstruction algorithm when the statistics of the sparse signal are non-Gaussian or unknown. The proposed method outperforms the widely used convex relaxation approaches as well as greedy matching pursuit techniques all while operating at a much lower complexity.

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U2 - 10.1109/Allerton.2011.6120179

DO - 10.1109/Allerton.2011.6120179

M3 - Conference contribution

AN - SCOPUS:84856112816

SN - 9781457718168

SP - 277

EP - 283

BT - 2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011

T2 - 2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011

Y2 - 28 September 2011 through 30 September 2011

ER -

Structure based Bayesian sparse reconstruction using non-Gaussian prior

Abstract

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