An insight into the issue of dimensionality in particle filtering

Paul Bui Quang 1, 2 Christian Musso 1 François Le Gland 2
2 ASPI - Applications of interacting particle systems to statistics
UR1 - Université de Rennes 1, Inria Rennes – Bretagne Atlantique , CNRS - Centre National de la Recherche Scientifique : UMR6074
Abstract : Particle filtering is a widely used Monte Carlo method to approximate the posterior density in non-linear filtering. Unlike the Kalman filter, the particle filter deals with non-linearity, multi-modality or non Gaussianity. However, recently, it has been observed that particle filtering can be inefficient when the dimension of the system is high. We discuss the effect of dimensionality on the Monte Carlo error and we analyze it in the case of a linear tracking model. In this case, we show that this error increases exponentially with the dimension.
Type de document :
Communication dans un congrès
Proceedings of the 13th International Conference on Information Fusion, Edinburgh 2010, Jul 2010, Edinburgh, United Kingdom. 2010, 〈10.1109/ICIF.2010.5712050〉
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https://hal.inria.fr/hal-00911994
Contributeur : Francois Le Gland <>
Soumis le : dimanche 1 décembre 2013 - 12:53:43
Dernière modification le : jeudi 11 janvier 2018 - 06:20:08

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Paul Bui Quang, Christian Musso, François Le Gland. An insight into the issue of dimensionality in particle filtering. Proceedings of the 13th International Conference on Information Fusion, Edinburgh 2010, Jul 2010, Edinburgh, United Kingdom. 2010, 〈10.1109/ICIF.2010.5712050〉. 〈hal-00911994〉

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