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Conference Paper: A new noise-constrained normalized least mean squares adaptive filtering algorithm

TitleA new noise-constrained normalized least mean squares adaptive filtering algorithm
Authors
KeywordsAdaptive Filtering
Adaptive Filters
Convergence Of Numerical Methods
Impulse Noise
Impulse Response
Issue Date2008
Citation
Ieee Asia-Pacific Conference On Circuits And Systems, Proceedings, Apccas, 2008, p. 197-200 How to Cite?
AbstractThis paper proposes a new noise-constrained normalized least mean squares (NC-NLMS) adaptive filtering algorithm and studies its mean and mean square convergence behaviors. The new NC-NLMS algorithm is obtained by extending the noise-constrained LMS (NC-LMS) algorithm of Wei [1], which was proposed to explore the prior information on the noise variance in identifying unknown finite impulse response channels. It gives rise to a variable step-size LMS algorithm that is capable of achieving better tradeoff between the requirements to maximize convergence rate and to minimize misadjustment. Using a novel transformation approach, a new NC-NLMS algorithm is derived based on the NC-LMS framework. Additionally, robust statistics technique is employed to improve the robustness of the NC-NLMS algorithm in impulsive noise environment. Simulation shows that the proposed NC-NLMS offers improved performance than the NC-LMS algorithm due to the data normalization and its robust version can achieve satisfactory performance against impulse noise. Extension to the least M-estimate (LMM) and normalized least M-estimate (NLMM) algorithms were also proposed. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/143330
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorZhou, Yen_HK
dc.contributor.authorHu, Yen_HK
dc.date.accessioned2011-11-22T08:30:36Z-
dc.date.available2011-11-22T08:30:36Z-
dc.date.issued2008en_HK
dc.identifier.citationIeee Asia-Pacific Conference On Circuits And Systems, Proceedings, Apccas, 2008, p. 197-200en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143330-
dc.description.abstractThis paper proposes a new noise-constrained normalized least mean squares (NC-NLMS) adaptive filtering algorithm and studies its mean and mean square convergence behaviors. The new NC-NLMS algorithm is obtained by extending the noise-constrained LMS (NC-LMS) algorithm of Wei [1], which was proposed to explore the prior information on the noise variance in identifying unknown finite impulse response channels. It gives rise to a variable step-size LMS algorithm that is capable of achieving better tradeoff between the requirements to maximize convergence rate and to minimize misadjustment. Using a novel transformation approach, a new NC-NLMS algorithm is derived based on the NC-LMS framework. Additionally, robust statistics technique is employed to improve the robustness of the NC-NLMS algorithm in impulsive noise environment. Simulation shows that the proposed NC-NLMS offers improved performance than the NC-LMS algorithm due to the data normalization and its robust version can achieve satisfactory performance against impulse noise. Extension to the least M-estimate (LMM) and normalized least M-estimate (NLMM) algorithms were also proposed. © 2008 IEEE.en_HK
dc.languageengen_US
dc.relation.ispartofIEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCASen_HK
dc.subjectAdaptive Filteringen_US
dc.subjectAdaptive Filtersen_US
dc.subjectConvergence Of Numerical Methodsen_US
dc.subjectImpulse Noiseen_US
dc.subjectImpulse Responseen_US
dc.titleA new noise-constrained normalized least mean squares adaptive filtering algorithmen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_HK
dc.identifier.emailZhang, ZG: zhangzg@hku.hken_HK
dc.identifier.emailZhou, Y: yizhou@eee.hku.hken_HK
dc.identifier.emailHu, Y: yhud@hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityZhang, ZG=rp01565en_HK
dc.identifier.authorityZhou, Y=rp00213en_HK
dc.identifier.authorityHu, Y=rp00432en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/APCCAS.2008.4745994en_HK
dc.identifier.scopuseid_2-s2.0-62949112559en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-62949112559&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage197en_HK
dc.identifier.epage200en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK
dc.identifier.scopusauthoridZhou, Y=55209555200en_HK
dc.identifier.scopusauthoridHu, Y=7407116091en_HK

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