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Article: A new sequential block partial update normalized least mean M-estimate algorithm and its convergence performance analysis

TitleA new sequential block partial update normalized least mean M-estimate algorithm and its convergence performance analysis
Authors
KeywordsAdaptive filtering
Impulsive noise
Robust statistics
Sequential partial update
Issue Date2010
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/content/120889/
Citation
Journal Of Signal Processing Systems, 2010, v. 58 n. 2, p. 173-191 How to Cite?
AbstractThis paper proposes a new sequential block partial update normalized least mean square (SBP-NLMS) algorithm and its nonlinear extension, the SBP-normalized least mean M-estimate (SBP-NLMM) algorithm, for adaptive filtering. These algorithms both utilize the sequential partial update strategy as in the sequential least mean square (S-LMS) algorithm to reduce the computational complexity. Particularly, the SBP-NLMM algorithm minimizes the M-estimate function for improved robustness to impulsive outliers over the SBP-NLMS algorithm. The convergence behaviors of these two algorithms under Gaussian inputs and Gaussian and contaminated Gaussian (CG) noises are analyzed and new analytical expressions describing the mean and mean square convergence behaviors are derived. The robustness of the proposed SBP-NLMM algorithm to impulsive noise and the accuracy of the performance analysis are verified by computer simulations. © 2009 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/155568
ISSN
2015 Impact Factor: 0.508
2015 SCImago Journal Rankings: 0.262
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZhou, Yen_HK
dc.contributor.authorHo, KLen_HK
dc.date.accessioned2012-08-08T08:34:08Z-
dc.date.available2012-08-08T08:34:08Z-
dc.date.issued2010en_HK
dc.identifier.citationJournal Of Signal Processing Systems, 2010, v. 58 n. 2, p. 173-191en_HK
dc.identifier.issn1939-8018en_HK
dc.identifier.urihttp://hdl.handle.net/10722/155568-
dc.description.abstractThis paper proposes a new sequential block partial update normalized least mean square (SBP-NLMS) algorithm and its nonlinear extension, the SBP-normalized least mean M-estimate (SBP-NLMM) algorithm, for adaptive filtering. These algorithms both utilize the sequential partial update strategy as in the sequential least mean square (S-LMS) algorithm to reduce the computational complexity. Particularly, the SBP-NLMM algorithm minimizes the M-estimate function for improved robustness to impulsive outliers over the SBP-NLMS algorithm. The convergence behaviors of these two algorithms under Gaussian inputs and Gaussian and contaminated Gaussian (CG) noises are analyzed and new analytical expressions describing the mean and mean square convergence behaviors are derived. The robustness of the proposed SBP-NLMM algorithm to impulsive noise and the accuracy of the performance analysis are verified by computer simulations. © 2009 Springer Science+Business Media, LLC.en_HK
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/content/120889/en_HK
dc.relation.ispartofJournal of Signal Processing Systemsen_HK
dc.subjectAdaptive filteringen_HK
dc.subjectImpulsive noiseen_HK
dc.subjectRobust statisticsen_HK
dc.subjectSequential partial updateen_HK
dc.titleA new sequential block partial update normalized least mean M-estimate algorithm and its convergence performance analysisen_HK
dc.typeArticleen_HK
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_HK
dc.identifier.emailZhou, Y: yizhou@eee.hku.hken_HK
dc.identifier.emailHo, KL: klho@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityZhou, Y=rp00213en_HK
dc.identifier.authorityHo, KL=rp00117en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/s11265-009-0346-3en_HK
dc.identifier.scopuseid_2-s2.0-77951205623en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77951205623&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume58en_HK
dc.identifier.issue2en_HK
dc.identifier.spage173en_HK
dc.identifier.epage191en_HK
dc.identifier.isiWOS:000273361100007-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZhou, Y=55209555200en_HK
dc.identifier.scopusauthoridHo, KL=7403581592en_HK
dc.identifier.citeulike4449331-

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