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Conference Paper: A new robust kalman filter algorithm under outliers and system uncertainties

TitleA new robust kalman filter algorithm under outliers and system uncertainties
Authors
KeywordsComputational Fluid Dynamics
Control Theory
Curve Fitting
Kalman Filters
Least Squares Approximations
Synthetic Aperture Sonar
Issue Date2005
PublisherIEEE.
Citation
Proceedings - Ieee International Symposium On Circuits And Systems, 2005, p. 4317-4320 How to Cite?
AbstractThis paper proposes a new robust Kalman filter algorithm under outliers and system uncertainties. The robust Kalman filter of Durovic and Kovacevic is extended to include unknown-but-bounded parameter uncertainties in the state or observation matrix. We first formulate the robust state estimation problem as an M-estimation problem, which leads to an unconstrained nonlinear optimization problem. This is then linearized and solved iteratively as a series of linear least-squares problem. These least-squares problems, subject to the bounded system uncertainties using the robust least squares method proposed by A. Ben-Tal and A. Nemirovski. Simulation results show that the new algorithm leads to a better performance than the conventional algorithms under outliers as well as system uncertainties. © 2005 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/45785
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorTse, KWen_HK
dc.date.accessioned2007-10-30T06:35:24Z-
dc.date.available2007-10-30T06:35:24Z-
dc.date.issued2005en_HK
dc.identifier.citationProceedings - Ieee International Symposium On Circuits And Systems, 2005, p. 4317-4320en_HK
dc.identifier.issn0271-4310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45785-
dc.description.abstractThis paper proposes a new robust Kalman filter algorithm under outliers and system uncertainties. The robust Kalman filter of Durovic and Kovacevic is extended to include unknown-but-bounded parameter uncertainties in the state or observation matrix. We first formulate the robust state estimation problem as an M-estimation problem, which leads to an unconstrained nonlinear optimization problem. This is then linearized and solved iteratively as a series of linear least-squares problem. These least-squares problems, subject to the bounded system uncertainties using the robust least squares method proposed by A. Ben-Tal and A. Nemirovski. Simulation results show that the new algorithm leads to a better performance than the conventional algorithms under outliers as well as system uncertainties. © 2005 IEEE.en_HK
dc.format.extent203117 bytes-
dc.format.extent3821 bytes-
dc.format.extent27162 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofProceedings - IEEE International Symposium on Circuits and Systemsen_HK
dc.rights©2005 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.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectComputational Fluid Dynamicsen_HK
dc.subjectControl Theory-
dc.subjectCurve Fitting-
dc.subjectKalman Filters-
dc.subjectLeast Squares Approximations-
dc.subjectSynthetic Aperture Sonar-
dc.titleA new robust kalman filter algorithm under outliers and system uncertaintiesen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0271-4302&volume=5&spage=4317&epage=4320&date=2005&atitle=A+new+robust+Kalman+filter+algorithm+under+outliers+and+system+uncertaintiesen_HK
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_HK
dc.identifier.emailZhang, ZG: zhangzg@hku.hken_HK
dc.identifier.emailTse, KW: kwtse@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityZhang, ZG=rp01565en_HK
dc.identifier.authorityTse, KW=rp00180en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ISCAS.2005.1465586en_HK
dc.identifier.scopuseid_2-s2.0-38149063057en_HK
dc.identifier.hkuros103016-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-38149063057&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage4317en_HK
dc.identifier.epage4320en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK
dc.identifier.scopusauthoridTse, KW=7102609851en_HK

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