This article is cited in 3 papers

Suboptimal filtering in stochastic systems with random parameters and unsolved derivatives

I. N. Sinitsyn<sup>ab

a</sup> Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
^b Moscow State Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125933, Russian Federation

Abstract: For observable Gaussian differential stochastic systems (StS) with random parameters in the form of integral canonical expansions (ICE) and StS with unsolved derivatives (USD), methodological support for synthesis of suboptimal filters is presented. Survey in fields of analytical modeling and suboptimal filtering (SOF), extrapolation, and identification is presented. Necessary information concerning multicomponent (MC) ICE is given. Special attention is paid to mean square regressive linearization including MC ICE. Basic results in normal SOF (NSOF) are presented for StS USD reducible to differential StS. Stationary and nonstationary NSOF are considered. An illustrative example for scalar StS USD reducible to differential is given. For future, SOF generalization methods of moments, quasi-moments, and one- and multidimensional densities of orthogonal expansions are recommended.

Keywords: normal approximation method (NAM), regression linearization, stochastic process, stochastic systems with unsolved derivatives (StS USD), suboptimal filtering (SOF).

Received: 26.09.2023

DOI: 10.14357/19922264240101