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Conference Paper: A new regularized QRD recursive least M-estimate algorithm: Performance analysis and applications

TitleA new regularized QRD recursive least M-estimate algorithm: Performance analysis and applications
Authors
KeywordsAcoustic echo cancellation
Adaptive filtering algorithms
Convergence analysis
Convergence performance
Double talk
Issue Date2010
PublisherIEEE.
Citation
1St International Conference On Green Circuits And Systems, Icgcs 2010, 2010, p. 190-195 How to Cite?
AbstractThis paper proposes a new regularized QR decomposition based recursive least M-estimate (R-QRRLM) adaptive filtering algorithm and studies its mean and mean square convergence performance and application to acoustic echo cancellation (AEC). The proposed algorithm extends the conventional RLM algorithm by imposing a weighted L2 regularization term on the coefficients to reduce the variance of the estimator. Moreover, a QRD-based algorithm is employed for efficient recursive implementation and improved numerical property. The mean convergence analysis shows that a bias solution to the classical Wiener solution will be introduced due to the regularization. The steady-state excess mean square error (EMSE) is derived and it suggests that the variance will decrease while the bias will increase with the regularization parameter. Therefore, regularization can help to trade bias for variance. In this study, the regularization parameter can be adaptively selected and the resultant variable regularization parameter QRRLM (VR-QRRLM) algorithm can obtain both high immunity to input variation and low steady-state EMSE values. The theoretical results are in good agreement with simulation results. Computer simulation results on AEC show that the R-QRRLM and VR-QRRLM algorithms considerably outperform the traditional RLS algorithm when the input signal level is low or during double talk. © 2010 IEEE.
DescriptionProceedings of the International Conference on Green Circuits and Systems, 2010, p. 190-195
Persistent Identifierhttp://hdl.handle.net/10722/126116
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorChu, YJen_HK
dc.contributor.authorZhang, ZGen_HK
dc.date.accessioned2010-10-31T12:10:39Z-
dc.date.available2010-10-31T12:10:39Z-
dc.date.issued2010en_HK
dc.identifier.citation1St International Conference On Green Circuits And Systems, Icgcs 2010, 2010, p. 190-195en_HK
dc.identifier.isbn978-1-4244-6876-8-
dc.identifier.urihttp://hdl.handle.net/10722/126116-
dc.descriptionProceedings of the International Conference on Green Circuits and Systems, 2010, p. 190-195-
dc.description.abstractThis paper proposes a new regularized QR decomposition based recursive least M-estimate (R-QRRLM) adaptive filtering algorithm and studies its mean and mean square convergence performance and application to acoustic echo cancellation (AEC). The proposed algorithm extends the conventional RLM algorithm by imposing a weighted L2 regularization term on the coefficients to reduce the variance of the estimator. Moreover, a QRD-based algorithm is employed for efficient recursive implementation and improved numerical property. The mean convergence analysis shows that a bias solution to the classical Wiener solution will be introduced due to the regularization. The steady-state excess mean square error (EMSE) is derived and it suggests that the variance will decrease while the bias will increase with the regularization parameter. Therefore, regularization can help to trade bias for variance. In this study, the regularization parameter can be adaptively selected and the resultant variable regularization parameter QRRLM (VR-QRRLM) algorithm can obtain both high immunity to input variation and low steady-state EMSE values. The theoretical results are in good agreement with simulation results. Computer simulation results on AEC show that the R-QRRLM and VR-QRRLM algorithms considerably outperform the traditional RLS algorithm when the input signal level is low or during double talk. © 2010 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.-
dc.relation.ispartof1st International Conference on Green Circuits and Systems, ICGCS 2010en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsInternational Conference on Green Circuits and Systems (ICGCS). Copyright © IEEE.-
dc.rights©2010 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.-
dc.subjectAcoustic echo cancellation-
dc.subjectAdaptive filtering algorithms-
dc.subjectConvergence analysis-
dc.subjectConvergence performance-
dc.subjectDouble talk-
dc.titleA new regularized QRD recursive least M-estimate algorithm: Performance analysis and applicationsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-1-4244-6876-8&volume=&spage=190&epage=195&date=2010&atitle=A+new+regularized+QRD+recursive+least+M-estimate+algorithm:+Performance+analysis+and+applications-
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.emailZhang, ZG:zgzhang@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityZhang, ZG=rp01565en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICGCS.2010.5543069en_HK
dc.identifier.scopuseid_2-s2.0-77956570553en_HK
dc.identifier.hkuros174342en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77956570553&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage190en_HK
dc.identifier.epage195en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridChu, YJ=35098281800en_HK
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK

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