On Persistence of Convergence of Kernel Density Estimates in Particle Filtering

Authors: David Coufal
Publication year: 2020
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: Information Technology, Systems Research, and Computational Physics
Bibliographic citation: COUFAL, David. On Persistence of Convergence of Kernel Density Estimates in Particle Filtering. In: KULCZYCKI, P., KACPRZYK, J., KÓCZY, L. T., MESIAR, R., WISNIEWSKI, R., eds. Information Technology, Systems Research, and Computational Physics. Cham: Springer, 2020, S. 339-346. Advances in Intelligent Systems and Computing, 945. ISBN 978-3-030-18057-7. ISSN 2194-5357
Dostupné z: doi:10.1007/978-3-030-18058-4_27
DOI: 10.1007/978-3-030-18058-4_27
ID: 505260

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.