dc.creatorDo Val J.B.R.
dc.creatorCosta E.F.
dc.date2000
dc.date2015-06-30T19:48:50Z
dc.date2015-11-26T14:46:37Z
dc.date2015-06-30T19:48:50Z
dc.date2015-11-26T14:46:37Z
dc.date.accessioned2018-03-28T21:56:27Z
dc.date.available2018-03-28T21:56:27Z
dc.identifier
dc.identifierProceedings Of The Ieee Conference On Decision And Control. , v. 4, n. , p. 3801 - 3806, 2000.
dc.identifier1912216
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-0034439773&partnerID=40&md5=f6f2e36807c358babe27ccc720c75dfd
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/107044
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/107044
dc.identifier2-s2.0-0034439773
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1252919
dc.descriptionThe paper presents a state predictor for linear time-varying systems using Kalman filter with the receding horizon strategy. It can be seen as a standard Kalman filter which takes into account the most recent data, those included in a moving data window of fixed length. The main purpose here is to assure stability for this type of filter. Under standard conditions we can establish a minimum horizon length for which the closed-loop filter with the receding horizon gain is exponentially stable. The approach makes no direct reference to the properties of the underlying Riccati equation, which allow us to address more general problems that can not be coined in terms of Riccati equations.
dc.description4
dc.description
dc.description3801
dc.description3806
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dc.languageen
dc.publisher
dc.relationProceedings of the IEEE Conference on Decision and Control
dc.rightsfechado
dc.sourceScopus
dc.titleStability Of Receding Horizon Kalman Filter In State Estimation Of Linear Time-varying Systems
dc.typeActas de congresos


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