Generalized maximum correntropy algorithm with affine projection for robust filtering under impulsive-noise environments

Zhao, J; Zhang, H; Zhang, JA

Generalized maximum correntropy algorithm with affine projection for robust filtering under impulsive-noise environments

Zhao, J Zhang, H Zhang, JA

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Signal Processing, 2020, 172
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Zhao, J
dc.contributor.author	Zhang, H
dc.contributor.author	Zhang, JA
dc.date.accessioned	2021-01-11T00:33:27Z
dc.date.available	2021-01-11T00:33:27Z
dc.date.issued	2020-07-01
dc.identifier.citation	Signal Processing, 2020, 172
dc.identifier.issn	0165-1684
dc.identifier.issn	1872-7557
dc.identifier.uri	http://hdl.handle.net/10453/145264
dc.description.abstract	© 2020 Combining affine projection (AP) with the generalized maximum correntropy (GMC) criterion, we propose a new family of AP-type filtering algorithms, called as APGMC, for system identification under impulsive-noise environments. By optimizing GMC of the a posterior error vector with a ℓ2-norm constraint on the filter weight vector, APGMC avoids the computation of the inversion of the input data matrix. Simulation results validate that APGMC achieves better filtering accuracy and faster convergence rate, compared to state-of-the-art algorithms.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Signal Processing
dc.relation.isbasedon	10.1016/j.sigpro.2020.107524
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Networking & Telecommunications
dc.title	Generalized maximum correntropy algorithm with affine projection for robust filtering under impulsive-noise environments
dc.type	Journal Article
utslib.citation.volume	172
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
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
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-11T00:33:02Z
pubs.publication-status	Published
pubs.volume	172

Abstract:

© 2020 Combining affine projection (AP) with the generalized maximum correntropy (GMC) criterion, we propose a new family of AP-type filtering algorithms, called as APGMC, for system identification under impulsive-noise environments. By optimizing GMC of the a posterior error vector with a ℓ2-norm constraint on the filter weight vector, APGMC avoids the computation of the inversion of the input data matrix. Simulation results validate that APGMC achieves better filtering accuracy and faster convergence rate, compared to state-of-the-art algorithms.

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http://hdl.handle.net/10453/145264