Informatics and Applications
2025, Volume 19, Issue 2, pp 27-34
COMPLEX STATISTICAL CRITERION CONDITIONALLY OPTIMAL FILTERING METHODS FOR OBSERVABLE IMPLICIT STOCHASTIC SYSTEMS
Abstract
Paper is devoted to exact and approximate based on complex statistical criterion (CSC) conditionally- optimal filtering (COF) methods for continuous and discrete observable implicit non-Gaussian stochastic systems (StS) reducible to explicit. Survey of COF based on mean square criterion for explicit and implicit StS and CSC COF for explicit StS is given. Reduction methods for smooth and discontinuous implicit functions are presented.
The CSC COF exact synthesis methods for reducible differential, regression, and autoregression StS are developed.
For reduced StS with additive Gaussian noises and statistically linearized implicit functions generalization of Kalman and Kalman-Bucy filters is considered. Some future generalizations of exact and approximate CSC COF are mentioned.
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[+] About this article
Title
COMPLEX STATISTICAL CRITERION CONDITIONALLY OPTIMAL FILTERING METHODS FOR OBSERVABLE IMPLICIT STOCHASTIC SYSTEMS
Journal
Informatics and Applications
2025, Volume 19, Issue 2, pp 27-34
Cover Date
2025-07-10
DOI
10.14357/19922264250204
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
conditionally-optimal (in Pugachev sense) filter; normal approximation method (NAM); normal suboptimal filter (NSOF); stochastic process (StP); stochastic system (StS)
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
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