Informatics and Applications
2026, Volume 20, Issue 1, pp 12-18
NORMAL FILTERING METHODS FOR OBSERVED HEREDITIARY STOCHASTIC SYSTEMS WITH UNSOLVED DERIVATIVES
Abstract
The paper presents analytical synthesis methods for normal conditionally optimal and suboptimal filters (NCOF and NSOF) based on the mean-square criterion (i. e., in the Pugachev sense). These methods are developed for information processing in interconnected, observable hereditary stochastic systems with unsolved derivatives (HStSUSD). Abrief survey of publications on the analysis, modeling, and nonlinear filtration in HStSUSD is also provided. The NCOF are based on a dual procedure of HStSUSD reduction to finite-differential stochastic systems using the methods of normal approximation and statistical linearization. The analytical reduction methods of first and second stages are discussed. To illustrate the approach, examples are presented where NSOF for HStSUSD is generalized throught the application of the second-stage Kalman-Bucy filtering techniques. The NCOF and NSOF peculiarities for real time filtering in reducible HStSUSD are outlined. Future generalizations are discussed.
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[+] About this article
Title
NORMAL FILTERING METHODS FOR OBSERVED HEREDITIARY STOCHASTIC SYSTEMS WITH UNSOLVED DERIVATIVES
Journal
Informatics and Applications
2026, Volume 20, Issue 1, pp 12-18
Cover Date
2026-01-04
DOI
10.14357/19922264260102
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
hereditary stochastic systems with unsolved derivatives (HStSUSD); normal conditionally optimal filter (NCOF); normal supoptimal filter (NSOF); stochastic process (StP)
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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