Informatics and Applications
2024, Volume 18, Issue 1, pp 2-10
SUBOPTIMAL FILTERING IN STOCHASTIC SYSTEMS WITH RANDOM PARAMETERS AND UNSOLVED DERIVATIVES
Abstract
For observable Gaussian differential stochastic systems (StS) with random parameters in the form of integral canonical expansions (ICE) and StS with unsolved derivatives (USD), methodological support for synthesis of suboptimal filters is presented. Survey in fields of analytical modeling and suboptimal filtering (SOF), extrapolation, and identification is presented. Necessary information concerning multicomponent (MC) ICE is given. Special attention is paid to mean square regressive linearization including MC ICE. Basic results in normal SOF (NSOF) are presented for StS USD reducible to differential StS. Stationary and nonstationary NSOF are considered. An illustrative example for scalar StS USD reducible to differential is given. For future, SOF generalization methods of moments, quasi-moments, and one- and multidimensional densities of orthogonal expansions are recommended.
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[+] About this article
Title
SUBOPTIMAL FILTERING IN STOCHASTIC SYSTEMS WITH RANDOM PARAMETERS AND UNSOLVED DERIVATIVES
Journal
Informatics and Applications
2024, Volume 18, Issue 1, pp 2-10
Cover Date
2024-04-10
DOI
10.14357/19922264240101
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
normal approximation method (NAM); regression linearization; stochastic process; stochastic systems with unsolved derivatives (StS USD); suboptimal filtering (SOF)
Authors
I. N. Sinitsyn  , 
Author Affiliations
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
Moscow State Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125933, Russian Federation
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