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On Convergence of Kernel Density Estimates in Particle Filtering
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SYSNO ASEP 0469752 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title On Convergence of Kernel Density Estimates in Particle Filtering Author(s) Coufal, David (UIVT-O) RID, SAI, ORCID Source Title Kybernetika. - : Ústav teorie informace a automatizace AV ČR, v. v. i. - ISSN 0023-5954
Roč. 52, č. 5 (2016), s. 735-756Number of pages 22 s. Language eng - English Country CZ - Czech Republic Keywords Fourier analysis ; kernel methods ; particle filter Subject RIV BB - Applied Statistics, Operational Research Institutional support UIVT-O - RVO:67985807 UT WOS 000392351600005 EID SCOPUS 85008368382 DOI 10.14736/kyb-2016-5-0735 Annotation The paper deals with kernel density estimates of filtering densities in the particle filter. The convergence of the estimates is investigated by means of Fourier analysis. It is shown that the estimates converge to the theoretical filtering densities in the mean integrated squared error. An upper bound on the convergence rate is given. The result is provided under a certain assumption on the Sobolev character of the filtering densities. A sufficient condition is presented for the persistence of this Sobolev character over time. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2017
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