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Nonlinear Filtering with Transfer Operator

Parikshit Dutta 1, * Abhishek Halder 2 Raktim Bhattacharya 2
* Corresponding author
1 STEEP [2010-2015] - Sustainability transition, environment, economy and local policy [2010-2015]
Inria Grenoble - Rhône-Alpes, LJK [2007-2015] - Laboratoire Jean Kuntzmann [2007-2015]
Abstract : This paper presents a new nonlinear filtering algorithm that is shown to outperform state-of-the-art particle filters with resampling. Starting from the Itˆo stochastic differential equation, the proposed algorithm harnesses Karhunen- Lo'eve expansion to derive an approximate non-autonomous dynamical system, for which transfer operator based density computation can be performed in exact arithmetic. It is proved that the algorithm is asymptotically consistent in mean-square sense. Numerical results demonstrate that explicitly accounting prior dynamics entail significant performance improvement for nonlinear non-Gaussian estimation problems with infrequent measurement updates, as compared to the performance of particle filters.
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Submitted on : Tuesday, February 5, 2013 - 3:04:48 PM
Last modification on : Friday, July 17, 2020 - 11:38:59 AM
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Parikshit Dutta, Abhishek Halder, Raktim Bhattacharya. Nonlinear Filtering with Transfer Operator. IEEE American Control Conference, Jun 2013, Washington, D.C., United States. pp.3069-3074, ⟨10.1109/ACC.2013.6580302⟩. ⟨hal-00785159⟩



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