Anti-noise-folding regularized subspace pursuit recovery algorithm for noisy sparse signals

Yang, X; Cui, Q; Dutkiewicz, E; Huang, X; Tao, X; Fang, G

Anti-noise-folding regularized subspace pursuit recovery algorithm for noisy sparse signals

Yang, X Cui, Q Dutkiewicz, E

Huang, X

Tao, X Fang, G

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Publication Type:: Conference Proceeding
Citation:: IEEE Wireless Communications and Networking Conference, WCNC, 2014, pp. 275 - 280
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Yang, X	en_US
dc.contributor.author	Cui, Q	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.contributor.author	Huang, X https://orcid.org/0000-0001-6869-5732	en_US
dc.contributor.author	Tao, X	en_US
dc.contributor.author	Fang, G	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Wireless Communications and Networking Conference, WCNC, 2014, pp. 275 - 280	en_US
dc.identifier.isbn	9781479930838	en_US
dc.identifier.issn	1525-3511	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30954
dc.description.abstract	© 2014 IEEE. Denoising recovery algorithms are very important for the development of compressed sensing (CS) theory and its applications. Considering the noise present in both the original sparse signal x and the compressive measurements y, we propose a novel denoising recovery algorithm, named Regularized Subspace Pursuit (RSP). Firstly, by introducing a data pre-processing operation, the proposed algorithm alleviates the noise-folding effect caused by the noise added to x. Then, the indices of the nonzero elements in x are identified by regularizing the chosen columns of the measurement matrix. Afterwards, the chosen indices are updated by retaining only the largest entries in the Minimum Mean Square Error (MMSE) estimated signal. Simulation results show that, compared with the traditional orthogonal matching pursuit (OMP) algorithm, the proposed RSP algorithm increases the successful recovery rate (and reduces the reconstruction error) by up to 50% and 86% (35% and 65%) in high noise level scenarios and inadequate measurements scenarios, respectively.	en_US
dc.relation.ispartof	IEEE Wireless Communications and Networking Conference, WCNC	en_US
dc.relation.isbasedon	10.1109/WCNC.2014.6951980	en_US
dc.title	Anti-noise-folding regularized subspace pursuit recovery algorithm for noisy sparse signals	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Denoising recovery algorithms are very important for the development of compressed sensing (CS) theory and its applications. Considering the noise present in both the original sparse signal x and the compressive measurements y, we propose a novel denoising recovery algorithm, named Regularized Subspace Pursuit (RSP). Firstly, by introducing a data pre-processing operation, the proposed algorithm alleviates the noise-folding effect caused by the noise added to x. Then, the indices of the nonzero elements in x are identified by regularizing the chosen columns of the measurement matrix. Afterwards, the chosen indices are updated by retaining only the largest entries in the Minimum Mean Square Error (MMSE) estimated signal. Simulation results show that, compared with the traditional orthogonal matching pursuit (OMP) algorithm, the proposed RSP algorithm increases the successful recovery rate (and reduces the reconstruction error) by up to 50% and 86% (35% and 65%) in high noise level scenarios and inadequate measurements scenarios, respectively.

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http://hdl.handle.net/10453/30954