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Statements

Subject Item
n2:RIV%2F49777513%3A23520%2F10%3A43898280%21RIV14-MSM-23520___
rdf:type
skos:Concept n19:Vysledek
rdfs:seeAlso
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5712076
dcterms:description
The goal of the article is to describe a software framework designed for nonlinear state estimation of discrete-time dynamic systems. The framework was designed with the aim to facilitate implementation, testing and use of various nonlinear state estimation methods. The main strength of the framework is its versatility due to the possibility of either structural or probabilistic model description. Besides the well-known basic nonlinear estimation methods such as the extended Kalman filter, the divided difference filters and the unscented Kalman filter, the framework implements the particle filter with advanced features. As the framework is designed on the object oriented basis, further extension by user-specified nonlinear estimation algorithms is extremely easy. The paper describes the individual components of the framework, their key features and use. The paper demonstrates easy and natural application of the framework in target tracking which is illustrated in two examples - tracking a ship with unknown control and tracking three targets based on raw data. The goal of the article is to describe a software framework designed for nonlinear state estimation of discrete-time dynamic systems. The framework was designed with the aim to facilitate implementation, testing and use of various nonlinear state estimation methods. The main strength of the framework is its versatility due to the possibility of either structural or probabilistic model description. Besides the well-known basic nonlinear estimation methods such as the extended Kalman filter, the divided difference filters and the unscented Kalman filter, the framework implements the particle filter with advanced features. As the framework is designed on the object oriented basis, further extension by user-specified nonlinear estimation algorithms is extremely easy. The paper describes the individual components of the framework, their key features and use. The paper demonstrates easy and natural application of the framework in target tracking which is illustrated in two examples - tracking a ship with unknown control and tracking three targets based on raw data.
dcterms:title
Nonlinear estimation framework in target tracking Nonlinear estimation framework in target tracking
skos:prefLabel
Nonlinear estimation framework in target tracking Nonlinear estimation framework in target tracking
skos:notation
RIV/49777513:23520/10:43898280!RIV14-MSM-23520___
n3:aktivita
n15:S n15:P
n3:aktivity
P(1M0572), P(GA102/08/0442), S
n3:dodaniDat
n12:2014
n3:domaciTvurceVysledku
n4:5446937 n4:8131953 n4:5495628 n4:3313980
n3:druhVysledku
n13:D
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
275037
n3:idVysledku
RIV/49777513:23520/10:43898280
n3:jazykVysledku
n9:eng
n3:klicovaSlova
Particle filter.; UKF; EKF; Tracking; Bayesian approach; Nonlinear state and parameter estimation
n3:klicoveSlovo
n5:Nonlinear%20state%20and%20parameter%20estimation n5:Particle%20filter. n5:EKF n5:UKF n5:Tracking n5:Bayesian%20approach
n3:kontrolniKodProRIV
[91C39755078D]
n3:mistoKonaniAkce
Edinburgh, UK
n3:mistoVydani
Piscataway
n3:nazevZdroje
Proceedings of the 13th International Conference on Information Fusion
n3:obor
n21:BC
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
5
n3:projekt
n22:GA102%2F08%2F0442 n22:1M0572
n3:rokUplatneniVysledku
n12:2010
n3:tvurceVysledku
Straka, Ondřej Duník, Jindřich Flídr, Miroslav Blasch, Erik Šimandl, Miroslav
n3:typAkce
n10:WRD
n3:zahajeniAkce
2010-07-26+02:00
s:numberOfPages
8
n14:hasPublisher
IEEE
n16:isbn
978-0-9824438-1-1
n11:organizacniJednotka
23520