Duan, L. and Farmer, C. L. and Moroz, I. M. (2010) *Regularized particle filter with Langevin resampling step.* In: ICNAAM 2010. (Submitted)

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## Abstract

The solution of an inverse problem involves the estimation of variables and parameters values given by the statespace system. While a general (infinite-dimensional) optimal filter theory [1, 2] exists for nonlinear systems with Gaussian or non-Gaussian noise, applications rely on (finite-dimensional) suboptimal approximations to the optimal filter for practical implementations. The most widely-studied filters of this kind include the Regularized Particle Filter (RPF) [3, 4] and the Ensemble Square Root Filter (EnSRF) [5]. The latter is an ad-hoc approximation to the Bayes Filter, while the former is rigorously formulated, based upon the Glivenko-Cantelli theorem. By introducing a new global resampling step to the RPF, the EnSRF is proved to approximate the RPF in a special case.

Item Type: | Conference or Workshop Item (Paper) |
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Subjects: | D - G > General |

Research Groups: | Oxford Centre for Collaborative Applied Mathematics |

ID Code: | 1004 |

Deposited By: | Peter Hudston |

Deposited On: | 28 Oct 2010 14:40 |

Last Modified: | 09 Feb 2012 15:56 |

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