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Chapitre d'ouvrage

Probabilistic set-membership state estimator

Luc Jaulin 1
E3I2 - Extraction et Exploitation de l'Information en Environnements Incertains, STIC - Pôle STIC [Brest]
Abstract : Interval constraint propagation methods have been shown to be efficient, robust and reliable to solve difficult nonlinear bounded-error state estimation problems. However they are considered as unsuitable in a probabilistic context, where the approximation of a probability density function by a set cannot be accepted as reliable. This paper proposes a new probabilistic approach which makes it possible to use classical set-membership observers which are robust with respect to outliers. The approach is illustrated on a localization of robots in situations where there exist a large number of outliers.
Type de document :
Chapitre d'ouvrage
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Contributeur : Annick Billon-Coat <>
Soumis le : vendredi 2 mars 2012 - 17:03:35
Dernière modification le : lundi 20 juillet 2020 - 11:24:14



Luc Jaulin. Probabilistic set-membership state estimator. Mathematical Engineering, Springer-Verlag, Vol 3 p. 117-128, 2011, ⟨10.1007/978-3-642-15956-5⟩. ⟨hal-00676041⟩



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