Informatics and Applications
2015, Volume 9, Issue 2, pp 30-38
NORMAL PUGACHEV FILTERS FOR STATE LINEAR STOCHASTIC SYSTEMS
- I. N. Sinitsyn
- E. R. Korepanov
Abstract
The applied theory of analytical synthesis of normal conditionally optimal (Pugachev) filters (NPF) in state linear non-Gaussian stochastic systems (StS) is presented. Special attention is paid to NPF for differential StS satisfying Liptzer-Shiraev conditions based on the normal approximation of a posteriori density and quasi-linear NPF based on statistical linearization of nonlinear functions depending on observations. For StS of high dimension and real-time problems, NPF are more effective than the suboptimal filters. The NPF algorithms are the basis of the "StS-Filters" software tool. Test examples are given.
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[+] About this article
Title
NORMAL PUGACHEV FILTERS FOR STATE LINEAR STOCHASTIC SYSTEMS
Journal
Informatics and Applications
2015, Volume 9, Issue 2, pp 30-38
Cover Date
2015-02-30
DOI
10.14357/19922264150204
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
Liptser-Shiraev filter (LSF); Liptser-Shiraev conditions; normal approximation method (NAM) for a posteriori density; normal conditionally optimal Pugachev filter (NPF); stochastic systems (StS); state linear StS; statistical linearization method (SLM)
Authors
I. N. Sinitsyn  and E. R. Korepanov
Author Affiliations
Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian
Academy of Sciences, 44-2 Vavilov Str.,Moscow 119333, Russian Federation
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