Software for syntesis of discrete Pugachev filters for normal processes in hereditary stochastic systems

Algorithms for synthesis of discrete conditionally optimal (Pugachev) filters (PF) for normal processing in continuous and discrete linear and nonlinear hereditary stochastic systems (HStS) with Wiener and Poisson noises are considered. Introduction is devoted to the survey of analysis and modeling problems in HStS. It is stated that for real time processing, problems in informatics and control PF are effective in computer realization. The theorems for HStS reduction to differential StS and discrete StS are presented. Also, the theorems for discrete PF synthesis for normal processing in linear and nonlinear HStS are given. The HStS test shock examples for software tools “IDStS-Filtering” are represented.