About: Unscented Kalman filter with advanced adaptation of scaling parameter

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://dx.doi.org/10.1016/j.automatica.2014.08.030
Description	The paper deals with state estimation of the nonlinear stochastic systems by means of the unscented Kalman filter with a focus on specification of the sigma-points. Their position is influenced by two design parameters-the scaling parameter determining the spread of the sigma-points and a covariance matrix decomposition determining rotation of the sigma-points. In this paper, a choice of the scaling parameter is analyzed. It is shown that considering other values than the standard choice may lead to increased quality of the estimate, especially if the scaling parameter is adapted. Several different criteria for the adaptation are proposed and techniques to reduce computational costs of the adaptation are developed. The proposed algorithm of the unscented Kalman filter with advanced adaptation of the scaling parameter is illustrated in a numerical example. The paper deals with state estimation of the nonlinear stochastic systems by means of the unscented Kalman filter with a focus on specification of the sigma-points. Their position is influenced by two design parameters-the scaling parameter determining the spread of the sigma-points and a covariance matrix decomposition determining rotation of the sigma-points. In this paper, a choice of the scaling parameter is analyzed. It is shown that considering other values than the standard choice may lead to increased quality of the estimate, especially if the scaling parameter is adapted. Several different criteria for the adaptation are proposed and techniques to reduce computational costs of the adaptation are developed. The proposed algorithm of the unscented Kalman filter with advanced adaptation of the scaling parameter is illustrated in a numerical example. (en)
Title	Unscented Kalman filter with advanced adaptation of scaling parameter Unscented Kalman filter with advanced adaptation of scaling parameter (en)
skos:prefLabel	Unscented Kalman filter with advanced adaptation of scaling parameter Unscented Kalman filter with advanced adaptation of scaling parameter (en)
skos:notation	RIV/49777513:23520/14:43922813!RIV15-GA0-23520___
http://linked.open...avai/riv/aktivita	P S
http://linked.open...avai/riv/aktivity	P(EE2.3.30.0013), P(GC13-07058J), S
http://linked.open...iv/cisloPeriodika	10
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Šimandl, Miroslav Straka, Ondřej Duník, Jindřich
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Západočeská univerzita v Plzni / Fakulta aplikovaných věd
http://linked.open...dnocenehoVysledku	52028
http://linked.open...ai/riv/idVysledku	RIV/49777513:23520/14:43922813
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Scaling parameter; Adaptation; Unscented Kalman filter; Stochastic systems; Nonlinear filtering; State estimation (en)
http://linked.open.../riv/klicoveSlovo	Unscented Kalman filter State estimation Adaptation Stochastic systems Nonlinear filtering Scaling parameter
http://linked.open...odStatuVydavatele	US - Spojené státy americké
http://linked.open...ontrolniKodProRIV	[F64BC75384B3]
http://linked.open...i/riv/nazevZdroje	AUTOMATICA
http://linked.open...in/vavai/riv/obor	BC
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Excellence in human resources as a source of competitiveness Consistent Fusion in Networked Estimation Systems
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	50
http://linked.open...iv/tvurceVysledku	Straka, Ondřej Šimandl, Miroslav Duník, Jindřich
issn	0005-1098
number of pages	8 (xsd:int)
http://bibframe.org/vocab/doi	10.1016/j.automatica.2014.08.030
http://localhost/t...ganizacniJednotka	23520

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