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Article: Improved approximate QR-LS algorithms for adaptive filtering

TitleImproved approximate QR-LS algorithms for adaptive filtering
Authors
Issue Date2004
PublisherIEEE.
Citation
Ieee Transactions On Circuits And Systems Ii: Express Briefs, 2004, v. 51 n. 1, p. 29-39 How to Cite?
AbstractThis paper studies a class of O(N) approximate QR-based least squares (A-QR-LS) algorithm recently proposed by Liu in 1995. It is shown that the A-QR-LS algorithm is equivalent to a normalized LMS algorithm with time-varying stepsizes and element-wise normalization of the input signal vector. It reduces to the QR-LMS algorithm proposed by Liu et al. in 1998, when all the normalization constants are chosen as the Euclidean norm of the input signal vector. An improved transform-domain approximate QR-LS (TA-QR-LS) algorithm, where the input signal vector is first approximately decorrelated by some unitary transformations before the normalization, is proposed to improve its convergence for highly correlated signals. The mean weight vectors of the algorithms are shown to converge to the optimal Wiener solution if the weighting factor w of the algorithm is chosen between 0 and 1. New Givens rotations-based algorithms for the A-QR-LS, TA-QR-LS, and the QR-LMS algorithms are proposed to reduce their arithmetic complexities. This reduces the arithmetic complexity by a factor of 2, and allows square root-free versions of the algorithms be developed. The performances of the various algorithms are evaluated through computer simulation of a system identification problem and an acoustic echo canceller. © 2004 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/42957
ISSN
2006 Impact Factor: 0.922
2007 SCImago Journal Rankings: 1.092
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorYang, XXen_HK
dc.date.accessioned2007-03-23T04:35:28Z-
dc.date.available2007-03-23T04:35:28Z-
dc.date.issued2004en_HK
dc.identifier.citationIeee Transactions On Circuits And Systems Ii: Express Briefs, 2004, v. 51 n. 1, p. 29-39en_HK
dc.identifier.issn1057-7130en_HK
dc.identifier.urihttp://hdl.handle.net/10722/42957-
dc.description.abstractThis paper studies a class of O(N) approximate QR-based least squares (A-QR-LS) algorithm recently proposed by Liu in 1995. It is shown that the A-QR-LS algorithm is equivalent to a normalized LMS algorithm with time-varying stepsizes and element-wise normalization of the input signal vector. It reduces to the QR-LMS algorithm proposed by Liu et al. in 1998, when all the normalization constants are chosen as the Euclidean norm of the input signal vector. An improved transform-domain approximate QR-LS (TA-QR-LS) algorithm, where the input signal vector is first approximately decorrelated by some unitary transformations before the normalization, is proposed to improve its convergence for highly correlated signals. The mean weight vectors of the algorithms are shown to converge to the optimal Wiener solution if the weighting factor w of the algorithm is chosen between 0 and 1. New Givens rotations-based algorithms for the A-QR-LS, TA-QR-LS, and the QR-LMS algorithms are proposed to reduce their arithmetic complexities. This reduces the arithmetic complexity by a factor of 2, and allows square root-free versions of the algorithms be developed. The performances of the various algorithms are evaluated through computer simulation of a system identification problem and an acoustic echo canceller. © 2004 IEEE.en_HK
dc.format.extent370532 bytes-
dc.format.extent28672 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Circuits and Systems II: Express Briefsen_HK
dc.rights©2004 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleImproved approximate QR-LS algorithms for adaptive filteringen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1549-7747&volume=51&issue=1&spage=29&epage=39&date=2004&atitle=Improved+approximate+QR-LS+algorithms+for+adaptive+filteringen_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TCSII.2003.821514en_HK
dc.identifier.scopuseid_2-s2.0-4544320915en_HK
dc.identifier.hkuros90005-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-4544320915&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume51en_HK
dc.identifier.issue1en_HK
dc.identifier.spage29en_HK
dc.identifier.epage39en_HK
dc.identifier.isiWOS:000220363400007-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridYang, XX=7406506103en_HK

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