Non-linear Bayesian Filtering by Convolution Method Using Fast Fourier Transform

Huilong Zhang 1, 2
2 CQFD - Quality control and dynamic reliability
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
Abstract : Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. When the Gaussian assumptions are inadequate, the Kalman-type filters fail to be optimal. Classical filtering methods, such as the particle filter or Zakai filter can still be optimal as they provide not only the mean and covariance matrix estimations but also the conditional probability density of the state, given the observations. In this article, we propose a new method to calculate the filtering distribution. Our method is grid-based, and uses the convolution method to calculate the prediction step. The novelty of our approach is that we apply a fast Fourier transform technique to obtain a competitive numerical algorithm. Our approach is compared to classical methods such as UKF, EKF and particle filters.
Type de document :
Communication dans un congrès
14th International Conference on Fusion, Jul 2011, Chicago, United States. 2011
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https://hal.inria.fr/hal-00648104
Contributeur : Huilong Zhang <>
Soumis le : lundi 5 décembre 2011 - 10:35:49
Dernière modification le : jeudi 11 janvier 2018 - 06:22:11

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  • HAL Id : hal-00648104, version 1

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Huilong Zhang. Non-linear Bayesian Filtering by Convolution Method Using Fast Fourier Transform. 14th International Conference on Fusion, Jul 2011, Chicago, United States. 2011. 〈hal-00648104〉

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