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Article: New sequential partial-update least mean M-estimate algorithms for robust adaptive system identification in impulsive noise

TitleNew sequential partial-update least mean M-estimate algorithms for robust adaptive system identification in impulsive noise
Authors
KeywordsAdaptive echo cancellation (AEC)
Adaptive noise cancellation (ANC)
Double-talk
Impulsive noise
Least mean M-estimate (LMM)
Least mean square (LMS)
Partial-update adaptive filters
Performance analysis
System identification
Issue Date2011
PublisherIEEE. The Journal's web site is located at http://www.ewh.ieee.org/soc/ies/ties/index.html
Citation
IEEE Transactions on Industrial Electronics, 2011, v. 58 n. 9, p. 4455-4470 How to Cite?
AbstractThe sequential partial-update least mean square (S-LMS)-based algorithms are efficient methods for reducing the arithmetic complexity in adaptive system identification and other industrial informatics applications. They are also attractive in acoustic applications where long impulse responses are encountered. A limitation of these algorithms is their degraded performances in an impulsive noise environment. This paper proposes new robust counterparts for the S-LMS family based on M-estimation. The proposed sequential least mean M-estimate (S-LMM) family of algorithms employ nonlinearity to improve their robustness to impulsive noise. Another contribution of this paper is the presentation of a convergence performance analysis for the S-LMS/S-LMM family for Gaussian inputs and additive Gaussian or contaminated Gaussian noises. The analysis is important for engineers to understand the behaviors of these algorithms and to select appropriate parameters for practical realizations. The theoretical analyses reveal the advantages of input normalization and the M-estimation in combating impulsive noise. Computer simulations on system identification and joint active noise and acoustic echo cancellations in automobiles with double-talk are conducted to verify the theoretical results and the effectiveness of the proposed algorithms. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/139282
ISSN
2015 Impact Factor: 6.383
2015 SCImago Journal Rankings: 3.285
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZhou, Yen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorHo, KLen_HK
dc.date.accessioned2011-09-23T05:47:52Z-
dc.date.available2011-09-23T05:47:52Z-
dc.date.issued2011en_HK
dc.identifier.citationIEEE Transactions on Industrial Electronics, 2011, v. 58 n. 9, p. 4455-4470en_HK
dc.identifier.issn0278-0046en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139282-
dc.description.abstractThe sequential partial-update least mean square (S-LMS)-based algorithms are efficient methods for reducing the arithmetic complexity in adaptive system identification and other industrial informatics applications. They are also attractive in acoustic applications where long impulse responses are encountered. A limitation of these algorithms is their degraded performances in an impulsive noise environment. This paper proposes new robust counterparts for the S-LMS family based on M-estimation. The proposed sequential least mean M-estimate (S-LMM) family of algorithms employ nonlinearity to improve their robustness to impulsive noise. Another contribution of this paper is the presentation of a convergence performance analysis for the S-LMS/S-LMM family for Gaussian inputs and additive Gaussian or contaminated Gaussian noises. The analysis is important for engineers to understand the behaviors of these algorithms and to select appropriate parameters for practical realizations. The theoretical analyses reveal the advantages of input normalization and the M-estimation in combating impulsive noise. Computer simulations on system identification and joint active noise and acoustic echo cancellations in automobiles with double-talk are conducted to verify the theoretical results and the effectiveness of the proposed algorithms. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://www.ewh.ieee.org/soc/ies/ties/index.htmlen_HK
dc.relation.ispartofIEEE Transactions on Industrial Electronicsen_HK
dc.rightsIEEE Transactions on Antennas and Propagation. Copyright © IEEE-
dc.rights©2011 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAdaptive echo cancellation (AEC)en_HK
dc.subjectAdaptive noise cancellation (ANC)en_HK
dc.subjectDouble-talken_HK
dc.subjectImpulsive noiseen_HK
dc.subjectLeast mean M-estimate (LMM)en_HK
dc.subjectLeast mean square (LMS)en_HK
dc.subjectPartial-update adaptive filtersen_HK
dc.subjectPerformance analysisen_HK
dc.subjectSystem identificationen_HK
dc.titleNew sequential partial-update least mean M-estimate algorithms for robust adaptive system identification in impulsive noiseen_HK
dc.typeArticleen_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.emailHo, KL:klho@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityHo, KL=rp00117en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TIE.2010.2098359en_HK
dc.identifier.scopuseid_2-s2.0-80051734143en_HK
dc.identifier.hkuros195837en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80051734143&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume58en_HK
dc.identifier.issue9en_HK
dc.identifier.spage4455en_HK
dc.identifier.epage4470en_HK
dc.identifier.isiWOS:000293920300074-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridZhou, Y=47562433300en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridHo, KL=7403581592en_HK

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