Systems and Means of Informatics

2025, Volume 35, Issue 1, pp 41-58

NORMAL SUBOPTIMAL FILTERING METHODS IN IMPLICIT OBSERVABLE GAUSSIAN STOCHASTIC SYSTEMS

  • I. N. Sinitsyn

Abstract

The article is dedicated to the theory of normal suboptimal filters (NSOF) and modified NSOF (MNSOF) for Gaussian continuous and discrete implicit stochastic systems (StS) reducible to explicit. It is supposed that observations do not influence the observable object and are described by explicit stochastic differential and difference equations. A short survey in the field of suboptimal NSOF (MNSOF) synthesis is given. Basic algorithms of NSOF (MNSOF) for the first type synthesis for nonlinear and quasi-linear reducible StS with smooth and nonsmooth implicit nonlinearities are described. The theory of NSOF of the second type for reducible implicit StS is developed on the basis of generalized Kalman-Bucy and Kalman filters. Such NSOF unlike NSOF (MNSOF) of the first type do not permit to estimate accuracy of filtering beforehand applications: quick (or real-time) information processing in technical or organization-technical-economical systems is described by small dimension equations when it is possible to neglect time constants at high derivatives (differences). The results are also applicable to implicit hereditary StS reducible to explicit differential (discrete) StS. Directions for future research are formulated.

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