Informatics and Applications
2024, Volume 18, Issue 4, pp 19-25
CONDITIONALLY OPTIMAL FILTERING AND EXTRAPOLATION FOR DIFFERENTIAL GAUSSIAN IMPLICIT STOCHASTIC SYSTEMS AT AUTOCORRELATED NOISE IN OBSERVATIONS
Abstract
The theory of Pugachev conditionally-optimal filtering and extrapolation of stochastic processes described
by explicit stochastic differential equations at autocorrelated noise in observations iswidely used inmodern real-time
information processing. The paper is devoted to implicitGaussian stochastic systems (StS) reducible to explicit StS
at observational autocorrelated noises. Themain results are: (i) typical mathematical models of observable implicit
StS reducible to differential explicit StS; (ii) basic equations for nonlinear conditionally-optimal filters (COF)
and conditionally-optimal extrapolators (COE) at noncorrelated and autocorrelated noises; and (iii) illustrative
examples for reducible StS. Main conclusions and perspective directions for COF and COE design in the case of
implicit differential and functional differential StS are given.
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[+] About this article
Title
CONDITIONALLY OPTIMAL FILTERING AND EXTRAPOLATION FOR DIFFERENTIAL GAUSSIAN IMPLICIT STOCHASTIC SYSTEMS AT AUTOCORRELATED NOISE IN OBSERVATIONS
Journal
Informatics and Applications
2024, Volume 18, Issue 4, pp 19-25
Cover Date
2024-12-26
DOI
10.14357/19922264240403
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
autocorrelated observation noise; conditionally-optimal extrapolation; conditionally-optimal filtering; explicit stochastic system; implicit stochastic system
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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