Abstract
A sufficient condition is provided for keeping the character of the filtering density in the filtering task. This is done for the Sobolev class of filtering densities. As a consequence, estimating the filtering density in particle filtering persists its convergence at any time of filtering. Specifying the condition complements previous results on using the kernel density estimates in particle filtering.
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Acknowledgements
This work was supported by programme CZ.02.1.01/0.0/0.0/16_013/0001787 (OP VVV) of the Ministry of Education, Youth and Sport of the Czech Republic, institutional support RVO:67985807 and grant SVV No. 260454 of Charles University.
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Coufal, D. (2020). On Persistence of Convergence of Kernel Density Estimates in Particle Filtering. In: Kulczycki, P., Kacprzyk, J., Kóczy, L., Mesiar, R., Wisniewski, R. (eds) Information Technology, Systems Research, and Computational Physics. ITSRCP 2018. Advances in Intelligent Systems and Computing, vol 945. Springer, Cham. https://doi.org/10.1007/978-3-030-18058-4_27
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DOI: https://doi.org/10.1007/978-3-030-18058-4_27
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