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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F12%3A00195666%21RIV13-GA0-21230___
rdf:type
n9:Vysledek skos:Concept
dcterms:description
Kalman filter is a frequently used tool for linear state estimation due to its simplicity and optimality. It can further be used for fusion of information obtained from multiple sensors. Kalman filtering is also often applied to nonlinear systems. As the direct application of bayesian functional recursion is computationally not feasible, approaches commonly taken use either a local approximation - Extended Kalman Filter based on linearization of the non-linear model - or the global one, as in the case of Particle Filters. An approach to the local approximation is the so called Unscented Kalman Filter. It is based on a set of symmetrically distributed sample points used to parameterise the mean and the covariance. Such filter is computationally simple and no linearization step is required. Another approach to selecting the set of sample points based on decorrelation of multivariable random variables and Hermite-Gauss Quadrature is introduced in this paper. This approachprovides an additional justification of the Unscented Kalman Filter development and provides further options to improve the accuracy of the approximation, particularly for polynomial nonlinearities. A detailed comparison of the two approaches is presented in the paper. Kalman filter is a frequently used tool for linear state estimation due to its simplicity and optimality. It can further be used for fusion of information obtained from multiple sensors. Kalman filtering is also often applied to nonlinear systems. As the direct application of bayesian functional recursion is computationally not feasible, approaches commonly taken use either a local approximation - Extended Kalman Filter based on linearization of the non-linear model - or the global one, as in the case of Particle Filters. An approach to the local approximation is the so called Unscented Kalman Filter. It is based on a set of symmetrically distributed sample points used to parameterise the mean and the covariance. Such filter is computationally simple and no linearization step is required. Another approach to selecting the set of sample points based on decorrelation of multivariable random variables and Hermite-Gauss Quadrature is introduced in this paper. This approachprovides an additional justification of the Unscented Kalman Filter development and provides further options to improve the accuracy of the approximation, particularly for polynomial nonlinearities. A detailed comparison of the two approaches is presented in the paper.
dcterms:title
UNSCENTED KALMAN FILTER REVISITED - HERMITE-GAUSS QUADRATURE APPROACH UNSCENTED KALMAN FILTER REVISITED - HERMITE-GAUSS QUADRATURE APPROACH
skos:prefLabel
UNSCENTED KALMAN FILTER REVISITED - HERMITE-GAUSS QUADRATURE APPROACH UNSCENTED KALMAN FILTER REVISITED - HERMITE-GAUSS QUADRATURE APPROACH
skos:notation
RIV/68407700:21230/12:00195666!RIV13-GA0-21230___
n9:predkladatel
n17:orjk%3A21230
n3:aktivita
n13:P
n3:aktivity
P(GAP103/11/1353)
n3:dodaniDat
n10:2013
n3:domaciTvurceVysledku
n4:3021580 n4:5325773
n3:druhVysledku
n20:D
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
176064
n3:idVysledku
RIV/68407700:21230/12:00195666
n3:jazykVysledku
n19:eng
n3:klicovaSlova
Kalman filtering; Bayesian recursion; nonlinear systems
n3:klicoveSlovo
n6:Bayesian%20recursion n6:Kalman%20filtering n6:nonlinear%20systems
n3:kontrolniKodProRIV
[DA1A304946E0]
n3:mistoKonaniAkce
Singapore
n3:mistoVydani
Piscataway
n3:nazevZdroje
Proceedings of 15th International Conference on Information Fusion
n3:obor
n12:BC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n21:GAP103%2F11%2F1353
n3:rokUplatneniVysledku
n10:2012
n3:tvurceVysledku
Štecha, Jan Havlena, Vladimír
n3:typAkce
n22:WRD
n3:zahajeniAkce
2012-07-09+02:00
s:numberOfPages
5
n14:hasPublisher
IEEE
n18:isbn
9780982443842
n15:organizacniJednotka
21230