Non-Gaussian prior Fast Bayesian Matching Pursuit

Mudassir Masood; Tareq Y. Al-Naffouri

Non-Gaussian prior Fast Bayesian Matching Pursuit

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

Abstract

A fast matching pursuit method (nGpFBMP) is introduced which performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian/unknown. It is agnostic on signal statistics and utilizes a greedy approach and order-recursive updates to determine the approximate MMSE estimate of the sparse signal. Simulation results demonstrate the power and robustness of the method.

Original language	English (US)
Title of host publication	15th. Saudi Technical Exchange Meeting (STEM)
State	Published - Dec 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

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https://repository.kaust.edu.sa/bitstream/10754/630844/1/nGpFBMP-STEM2012.pdf

Cite this

@inproceedings{35da0b14070a4867bc93813b46da77e9,

title = "Non-Gaussian prior Fast Bayesian Matching Pursuit",

abstract = "A fast matching pursuit method (nGpFBMP) is introduced which performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian/unknown. It is agnostic on signal statistics and utilizes a greedy approach and order-recursive updates to determine the approximate MMSE estimate of the sparse signal. Simulation results demonstrate the power and robustness of the method.",

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

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

year = "2012",

month = dec,

language = "English (US)",

booktitle = "15th. Saudi Technical Exchange Meeting (STEM)",

}

TY - GEN

T1 - Non-Gaussian prior Fast Bayesian Matching Pursuit

AU - Masood, Mudassir

AU - Al-Naffouri, Tareq Y.

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

PY - 2012/12

Y1 - 2012/12

N2 - A fast matching pursuit method (nGpFBMP) is introduced which performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian/unknown. It is agnostic on signal statistics and utilizes a greedy approach and order-recursive updates to determine the approximate MMSE estimate of the sparse signal. Simulation results demonstrate the power and robustness of the method.

AB - A fast matching pursuit method (nGpFBMP) is introduced which performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian/unknown. It is agnostic on signal statistics and utilizes a greedy approach and order-recursive updates to determine the approximate MMSE estimate of the sparse signal. Simulation results demonstrate the power and robustness of the method.

UR - http://hdl.handle.net/10754/630844

UR - http://faculty.kfupm.edu.sa/ee/naffouri/publications/nGpFBMP-STEM2012.pdf

M3 - Conference contribution

BT - 15th. Saudi Technical Exchange Meeting (STEM)

ER -

Non-Gaussian prior Fast Bayesian Matching Pursuit

Abstract

Bibliographical note

Access to Document

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