Informatics and Applications

2025, Volume 19, Issue 1, pp 44-51

NORMAL CONDITIONALLY-OPTIMAL FILTERING METHODS FOR IMPLICIT STOCHASTIC SYSTEMS

I. N. Sinitsyn

Abstract

The paper is dedicated to nonlinear synthesis of normal conditionally-optimal (in Pugachev sense) filters (NCOF) for information processing in interconnected observable implicit object stochastic systems (StS) reducible to explicit. Differential and difference equations of observable non-Gaussian StS are presented and methods of its reduction are considered. Special attention is paid to nonsmooth implicit StS. Differential NCOF equations are derived by methods of normal approximation (NAM) of the set of differential equations for object, observation system, and Pugachev conditionally-optimal filters. Difference NCOF equations based on NAM are given for nonlinear regression and autoregression StS models. Some generalizations for complex implicit StS and control StS are formulated.

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