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Conference Paper: A new family of approximate QR-LS algorithms for adaptive filtering

TitleA new family of approximate QR-LS algorithms for adaptive filtering
Authors
Issue Date2005
PublisherIEEE.
Citation
Ieee Workshop On Statistical Signal Processing Proceedings, 2005, v. 2005, p. 71-75 How to Cite?
AbstractThis paper proposes a new family of approximate QR-based least squares (LS) adaptive filtering algorithms called p-TA-QR-LS algorithms. It extends the TA-QR-LS algorithm [6] by retaining different number of diagonal plus off-diagonals (denoted by an integer p) of the triangular factor of the augmented data matrix. For p=1 and N, it reduces respectively to the TA-QR-LS and the QR-RLS algorithms. It not only provides a link between the QR-LMS-type and the QR-RLS algorithms through a well-structured family of algorithms, but also offers flexible complexity-performance tradeoffs in practical implementation. These results are verified by computer simulation and the mean convergence of the algorithms is also analyzed. © 2005 IEEE.
DescriptionThe 13th IEEE / S P Workshop on Statistical Signal Processing, Bordeaux, France, 17-20 July 2005
Persistent Identifierhttp://hdl.handle.net/10722/45921
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorZhou, Yen_HK
dc.contributor.authorChan, SCen_HK
dc.date.accessioned2007-10-30T06:38:31Z-
dc.date.available2007-10-30T06:38:31Z-
dc.date.issued2005en_HK
dc.identifier.citationIeee Workshop On Statistical Signal Processing Proceedings, 2005, v. 2005, p. 71-75en_HK
dc.identifier.isbn0-7803-9403-8en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45921-
dc.descriptionThe 13th IEEE / S P Workshop on Statistical Signal Processing, Bordeaux, France, 17-20 July 2005-
dc.description.abstractThis paper proposes a new family of approximate QR-based least squares (LS) adaptive filtering algorithms called p-TA-QR-LS algorithms. It extends the TA-QR-LS algorithm [6] by retaining different number of diagonal plus off-diagonals (denoted by an integer p) of the triangular factor of the augmented data matrix. For p=1 and N, it reduces respectively to the TA-QR-LS and the QR-RLS algorithms. It not only provides a link between the QR-LMS-type and the QR-RLS algorithms through a well-structured family of algorithms, but also offers flexible complexity-performance tradeoffs in practical implementation. These results are verified by computer simulation and the mean convergence of the algorithms is also analyzed. © 2005 IEEE.en_HK
dc.format.extent170069 bytes-
dc.format.extent27162 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Workshop on Statistical Signal Processing Proceedingsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2005 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.titleA new family of approximate QR-LS algorithms for adaptive filteringen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0-7803-9403-8&volume=&spage=71&epage=76&date=2005&atitle=A+new+family+of+approximate+QR-LS+algorithms+for+adaptive+filteringen_HK
dc.identifier.emailZhou, Y: yizhou@eee.hku.hken_HK
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_HK
dc.identifier.authorityZhou, Y=rp00213en_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.scopuseid_2-s2.0-33947102159en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33947102159&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume2005en_HK
dc.identifier.spage71en_HK
dc.identifier.epage75en_HK
dc.identifier.scopusauthoridZhou, Y=55209555200en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK

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