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Article: A new transform-domain regularized recursive least M-estimate algorithm for a robust linear estimation

TitleA new transform-domain regularized recursive least M-estimate algorithm for a robust linear estimation
Authors
KeywordsQR decomposition (QRD)
recursive linear estimation and filtering
regularization
smoothly clipped absolute deviation (SCAD)
system identification
transformed M-estimation (ME)
Issue Date2011
PublisherIEEE.
Citation
Ieee Transactions On Circuits And Systems Ii: Express Briefs, 2011, v. 58 n. 2, p. 120-124 How to Cite?
AbstractThis brief proposes a new transform-domain (TD) regularized M-estimation (TD-R-ME) algorithm for a robust linear estimation in an impulsive noise environment and develops an efficient QR-decomposition-based algorithm for recursive implementation. By formulating the robust regularized linear estimation in transformed regression coefficients, the proposed TD-R-ME algorithm was found to offer better estimation accuracy than direct application of regularization techniques to estimate system coefficients when they are correlated. Furthermore, a QR-based algorithm and an effective adaptive method for selecting regularization parameters are developed for recursive implementation of the TD-R-ME algorithm. Simulation results show that the proposed TD regularized QR recursive least M-estimate (TD-R-QRRLM) algorithm offers improved performance over its least squares counterpart in an impulsive noise environment. Moreover, a TD smoothly clipped absolute deviation R-QRRLM was found to give a better steady-state excess mean square error than other QRRLM-related methods when regression coefficients are correlated. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/143343
ISSN
2015 Impact Factor: 1.136
2015 SCImago Journal Rankings: 0.747
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong Kong Committee on Research and Conference
Funding Information:

Manuscript received July 15, 2010; revised October 20, 2010; accepted December 14, 2010. Date of current version February 24, 2011. This work was supported in part by The University of Hong Kong Committee on Research and Conference Grants Small Project Funding. This paper was recommended by Associate Editor Z. Lin.

References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorChu, YJen_HK
dc.date.accessioned2011-11-22T08:30:55Z-
dc.date.available2011-11-22T08:30:55Z-
dc.date.issued2011en_HK
dc.identifier.citationIeee Transactions On Circuits And Systems Ii: Express Briefs, 2011, v. 58 n. 2, p. 120-124en_HK
dc.identifier.issn1549-7747en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143343-
dc.description.abstractThis brief proposes a new transform-domain (TD) regularized M-estimation (TD-R-ME) algorithm for a robust linear estimation in an impulsive noise environment and develops an efficient QR-decomposition-based algorithm for recursive implementation. By formulating the robust regularized linear estimation in transformed regression coefficients, the proposed TD-R-ME algorithm was found to offer better estimation accuracy than direct application of regularization techniques to estimate system coefficients when they are correlated. Furthermore, a QR-based algorithm and an effective adaptive method for selecting regularization parameters are developed for recursive implementation of the TD-R-ME algorithm. Simulation results show that the proposed TD regularized QR recursive least M-estimate (TD-R-QRRLM) algorithm offers improved performance over its least squares counterpart in an impulsive noise environment. Moreover, a TD smoothly clipped absolute deviation R-QRRLM was found to give a better steady-state excess mean square error than other QRRLM-related methods when regression coefficients are correlated. © 2006 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE.en_US
dc.relation.ispartofIEEE Transactions on Circuits and Systems II: Express Briefsen_HK
dc.rightsIEEE Transactions on Circuits and Systems Part 2: Express Briefs. 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.subjectQR decomposition (QRD)en_HK
dc.subjectrecursive linear estimation and filteringen_HK
dc.subjectregularizationen_HK
dc.subjectsmoothly clipped absolute deviation (SCAD)en_HK
dc.subjectsystem identificationen_HK
dc.subjecttransformed M-estimation (ME)en_HK
dc.titleA new transform-domain regularized recursive least M-estimate algorithm for a robust linear estimationen_HK
dc.typeArticleen_HK
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_versionen_US
dc.identifier.doi10.1109/TCSII.2011.2106314en_HK
dc.identifier.scopuseid_2-s2.0-79952033745en_HK
dc.identifier.hkuros191164-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952033745&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume58en_HK
dc.identifier.issue2en_HK
dc.identifier.spage120en_HK
dc.identifier.epage124en_HK
dc.identifier.isiWOS:000287660600012-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK
dc.identifier.scopusauthoridChu, YJ=35098281800en_HK

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