Sparse reconstruction using distribution agnostic bayesian matching pursuit

Mudassir Masood; Tareq Y. Al-Naffouri

doi:10.1109/TSP.2013.2278814

Sparse reconstruction using distribution agnostic bayesian matching pursuit

Mudassir Masood, Tareq Y. Al-Naffouri

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78 Scopus citations

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.

Original language	English (US)
Pages (from-to)	5298-5309
Number of pages	12
Journal	IEEE Transactions on Signal Processing
Volume	61
Issue number	21
DOIs	https://doi.org/10.1109/TSP.2013.2278814
State	Published - Nov 2013

Bibliographical note

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

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

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10.1109/TSP.2013.2278814

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@article{2368792c349d49dc861a56dd4fa40603,

title = "Sparse reconstruction using distribution agnostic bayesian matching pursuit",

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. {\textcopyright} 2013 IEEE.",

author = "Mudassir Masood and Al-Naffouri, {Tareq Y.}",

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

year = "2013",

month = nov,

doi = "10.1109/TSP.2013.2278814",

language = "English (US)",

volume = "61",

pages = "5298--5309",

journal = "IEEE Transactions on Signal Processing",

issn = "1053-587X",

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

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AU - Masood, Mudassir

AU - Al-Naffouri, Tareq Y.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: 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.

PY - 2013/11

Y1 - 2013/11

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/563065

UR - http://arxiv.org/abs/arXiv:1206.4208v1

UR - http://www.scopus.com/inward/record.url?scp=84884862337&partnerID=8YFLogxK

U2 - 10.1109/TSP.2013.2278814

DO - 10.1109/TSP.2013.2278814

M3 - Article

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VL - 61

SP - 5298

EP - 5309

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 21

ER -

Sparse reconstruction using distribution agnostic bayesian matching pursuit

Abstract

Bibliographical note

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