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Conference Paper: An improved approximate QR-LS based second-order Volterra filter

TitleAn improved approximate QR-LS based second-order Volterra filter
Authors
Issue Date2003
PublisherIEEE.
Citation
The 2003 IEEE Workshop on Statistical Signal Processing, St. Louis, MO, USA, 28 September-1 October 2003. In Conference Proceedings, 2003, p. 214-217 How to Cite?
AbstractThis paper proposes a new transform-domain approximate QR least-squares-based (TA-QR-LS) algorithm for adaptive Volterra filtering (AVF). It improves the approximate QR least-squares (A-QR-LS) algorithm for multichannel adaptive filtering by introducing a unitary transformation to decorrelate the input signal vector so as to achieve better convergence and tracking performances. Further, the Givens rotation is used instead of the Householder transformation to reduce the arithmetic complexity. Simulation results show that the proposed algorithm has much better initial convergence and steady state performance than the LMS-based algorithm. The fast RLS AVF algorithm [J. Lee and V. J. Mathews, Mar 1993] was found to exhibit superior steady state performance when the forgetting factor is chosen to be 0.995, but the tracking performance of the TA-QR-LS algorithm was found to be considerably better.
Persistent Identifierhttp://hdl.handle.net/10722/46423

 

DC FieldValueLanguage
dc.contributor.authorZhou, Yen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorHo, KLen_HK
dc.date.accessioned2007-10-30T06:49:33Z-
dc.date.available2007-10-30T06:49:33Z-
dc.date.issued2003en_HK
dc.identifier.citationThe 2003 IEEE Workshop on Statistical Signal Processing, St. Louis, MO, USA, 28 September-1 October 2003. In Conference Proceedings, 2003, p. 214-217en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46423-
dc.description.abstractThis paper proposes a new transform-domain approximate QR least-squares-based (TA-QR-LS) algorithm for adaptive Volterra filtering (AVF). It improves the approximate QR least-squares (A-QR-LS) algorithm for multichannel adaptive filtering by introducing a unitary transformation to decorrelate the input signal vector so as to achieve better convergence and tracking performances. Further, the Givens rotation is used instead of the Householder transformation to reduce the arithmetic complexity. Simulation results show that the proposed algorithm has much better initial convergence and steady state performance than the LMS-based algorithm. The fast RLS AVF algorithm [J. Lee and V. J. Mathews, Mar 1993] was found to exhibit superior steady state performance when the forgetting factor is chosen to be 0.995, but the tracking performance of the TA-QR-LS algorithm was found to be considerably better.en_HK
dc.format.extent285133 bytes-
dc.format.extent8028 bytes-
dc.format.extent27162 bytes-
dc.format.extent2198 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE/SP Workshop on Statistical Signal Processing (SSP)-
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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleAn improved approximate QR-LS based second-order Volterra filteren_HK
dc.typeConference_Paperen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/SSP.2003.1289382en_HK
dc.identifier.hkuros90029-

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