This article is cited in 1 paper

Normal conditionally-optimal filtering methods for implicit stochastic systems

I. N. Sinitsyn

Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

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.

Keywords: conditionally-optimal (in Pugachev sense) filter, implicit stochastic system, normal approximation method (NAM), normal suboptimal filter (NSOF), stochastic process (StP).

Received: 10.10.2024
Accepted: 15.01.2025

DOI: 10.14357/19922264250106