Informatics and Applications
2022, Volume 16, Issue 4, pp 8-13
TOTAL APPROXIMATION ORDER FOR MARKOV JUMP PROCESS FILTERING GIVEN DISCRETIZED OBSERVATIONS
Abstract
The note proceeds the investigation devoted to the numerical approximation of the Markov jump process filtering given both the counting and diffusion observations with the multiplicative noise. The filtering estimates are approximated using the observations, previously discretized by time. By contrast with the previous algorithms which limit the number of the Markov state transitions that occurred during the time discretization interval, the new estimates are free of these restrictions and constructed via a unified scheme. The note presents an upper bound for the approximation accuracy as a function of the observation system parameters, applied scheme of the numerical integration, the time discretization step, and the filtering moment. A numerical example illustrates a sublinear character of the bound towards the latter argument.
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[+] About this article
Title
TOTAL APPROXIMATION ORDER FOR MARKOV JUMP PROCESS FILTERING GIVEN DISCRETIZED OBSERVATIONS
Journal
Informatics and Applications
2022, Volume 16, Issue 4, pp 8-13
Cover Date
2022-12-30
DOI
10.14357/19922264220402
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
Markov jump process; optimal filtering; diffusion and counting observations; multiplicative observation noise; numerical approximation accuracy
Authors
A. V. Borisov 
Author Affiliations
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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