Regularized Particle Filter with Langevin Resampling Step

Lian Duan, Chris L. Farmer, Irene M. Moroz

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The solution of an inverse problem involves the estimation of variables and parameters values given by the state-space 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.
Original languageEnglish (US)
Title of host publicationInternational Conference on Numerical Analysis and Applied Mathematics
Number of pages1079
StatePublished - 2010
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-23
Acknowledged KAUST grant number(s): KUK-C1-013-04
Acknowledgements: This publication was based on work supported in part by Award No KUK-C1-013-04 , made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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