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Conference Paper: Robust recursive bi-iteration singular value decomposition (SVD) for subspace tracking and adaptive filtering

TitleRobust recursive bi-iteration singular value decomposition (SVD) for subspace tracking and adaptive filtering
Authors
KeywordsElectronics
Issue Date2003
PublisherIEEE.
Citation
Proceedings - Ieee International Symposium On Circuits And Systems, 2003, v. 4, p. IV424-IV427 How to Cite?
AbstractThe recursive bi-iteration singular value decomposition (Bi-SVD), proposed by Strobach, is en efficient and well-structured algorithm for performing subspace tracking. Unfortunately, its performance under impulse noise environment degrades substantially. In this paper, a new robust-statistics-based bi-iteration SVD algorithm (robust Bi-SVD) is proposed. Simulation results show that the proposed algorithm offers significantly improved robustness against impulse noise than the conventional Bi-SVD algorithm with slight increase in arithmetic complexity. For nominal Gaussian noise, the two algorithms have similar performance.
DescriptionIEEE International Symposium on Circuits and Systems Proceedings, Bangkok, Thailand, 25-28 May 2003
Persistent Identifierhttp://hdl.handle.net/10722/46391
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorWen, Yen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorHo, KLen_HK
dc.date.accessioned2007-10-30T06:48:52Z-
dc.date.available2007-10-30T06:48:52Z-
dc.date.issued2003en_HK
dc.identifier.citationProceedings - Ieee International Symposium On Circuits And Systems, 2003, v. 4, p. IV424-IV427en_HK
dc.identifier.issn0271-4310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46391-
dc.descriptionIEEE International Symposium on Circuits and Systems Proceedings, Bangkok, Thailand, 25-28 May 2003-
dc.description.abstractThe recursive bi-iteration singular value decomposition (Bi-SVD), proposed by Strobach, is en efficient and well-structured algorithm for performing subspace tracking. Unfortunately, its performance under impulse noise environment degrades substantially. In this paper, a new robust-statistics-based bi-iteration SVD algorithm (robust Bi-SVD) is proposed. Simulation results show that the proposed algorithm offers significantly improved robustness against impulse noise than the conventional Bi-SVD algorithm with slight increase in arithmetic complexity. For nominal Gaussian noise, the two algorithms have similar performance.en_HK
dc.format.extent440543 bytes-
dc.format.extent8028 bytes-
dc.format.extent27162 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofProceedings - IEEE International Symposium on Circuits and Systemsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectElectronicsen_HK
dc.titleRobust recursive bi-iteration singular value decomposition (SVD) for subspace tracking and adaptive filteringen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0271-4302&volume=4&spage=424&epage=427&date=2003&atitle=Robust+recursive+bi-iteration+singular+value+decomposition+(SVD)+for+subspace+tracking+and+adaptive+filteringen_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.emailHo, KL:klho@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityHo, KL=rp00117en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ISCAS.2003.1205866en_HK
dc.identifier.scopuseid_2-s2.0-17644443427en_HK
dc.identifier.hkuros82318-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-17644443427&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4en_HK
dc.identifier.spageIV424en_HK
dc.identifier.epageIV427en_HK
dc.identifier.scopusauthoridWen, Y=55239414600en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridHo, KL=7403581592en_HK

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