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Conference Paper: State estimation with measurement error compensation using neural network

TitleState estimation with measurement error compensation using neural network
Authors
KeywordsKalman filter
Redundant sensors
Measurement compensation
Neural networks
Issue Date1998
PublisherIEEE.
Citation
Ieee Conference On Control Applications - Proceedings, 1998, v. 1, p. 153-157 How to Cite?
AbstractFor a system with redundant sensors, the estimated state from the Kalman filter is biased if sensor mounting error existed. To remove this bias, the mounting errors must be compensated first before using the Kalman filter. It is shown that only the projection part of the sensors errors in the measurement space needs to be compensated. If the state of a system is unavailable, a neurofuzzy network can be used to estimate the compensation term. This method is simpler, as it does not require a model for the errors as that proposed in [2]. A sub-optimal Kalman filter with measurement compensation that restrains each row of the Kalman gain matrix to be in the measurement space is also derived. An example is presented to illustrate the performance of the proposed methods.
Persistent Identifierhttp://hdl.handle.net/10722/46648
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChan, CWen_HK
dc.contributor.authorJin, Hen_HK
dc.contributor.authorCheung, KCen_HK
dc.contributor.authorZhang, HYen_HK
dc.date.accessioned2007-10-30T06:55:02Z-
dc.date.available2007-10-30T06:55:02Z-
dc.date.issued1998en_HK
dc.identifier.citationIeee Conference On Control Applications - Proceedings, 1998, v. 1, p. 153-157en_HK
dc.identifier.issn1085-1992en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46648-
dc.description.abstractFor a system with redundant sensors, the estimated state from the Kalman filter is biased if sensor mounting error existed. To remove this bias, the mounting errors must be compensated first before using the Kalman filter. It is shown that only the projection part of the sensors errors in the measurement space needs to be compensated. If the state of a system is unavailable, a neurofuzzy network can be used to estimate the compensation term. This method is simpler, as it does not require a model for the errors as that proposed in [2]. A sub-optimal Kalman filter with measurement compensation that restrains each row of the Kalman gain matrix to be in the measurement space is also derived. An example is presented to illustrate the performance of the proposed methods.en_HK
dc.format.extent496811 bytes-
dc.format.extent5145 bytes-
dc.format.extent3469 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Conference on Control Applications - Proceedingsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1998 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.subjectKalman filteren_HK
dc.subjectRedundant sensorsen_HK
dc.subjectMeasurement compensationen_HK
dc.subjectNeural networksen_HK
dc.titleState estimation with measurement error compensation using neural networken_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1085-1992&volume=1&spage=153&epage=157&date=1998&atitle=State+estimation+with+measurement+error+compensation+using+neural+networken_HK
dc.identifier.emailChan, CW:mechan@hkucc.hku.hken_HK
dc.identifier.emailCheung, KC:kccheung@hkucc.hku.hken_HK
dc.identifier.authorityChan, CW=rp00088en_HK
dc.identifier.authorityCheung, KC=rp01322en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/CCA.1998.728315en_HK
dc.identifier.scopuseid_2-s2.0-0032299627en_HK
dc.identifier.hkuros41235-
dc.identifier.volume1en_HK
dc.identifier.spage153en_HK
dc.identifier.epage157en_HK
dc.identifier.scopusauthoridChan, CW=7404814060en_HK
dc.identifier.scopusauthoridJin, H=34770583400en_HK
dc.identifier.scopusauthoridCheung, KC=7402406698en_HK
dc.identifier.scopusauthoridZhang, HY=7409196387en_HK

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