Systems and Means of Informatics
2016, Volume 26, Issue 2, pp 63-78
NORMAL PUGACHEV FILTERS FOR STATE LINEAR AUTOCORRELATED STOCHASTIC SYSTEMS
- I. N. Sinitsyn
- E. R. Korepanov
Abstract
The analytical synthesis theory of continuous conditionally optimal Pugachev filters for processing in linear state stochastic systems (StS) with uncorrelated and autocorrelated noises is presented. For non-Gaussian StS, first works belong to Pugachev and Sinitsyn. Basic algorithms for state linear StS with uncorrelated noises are given. Generalization of algorithms for autocorrelated state linear Sts is presented. A test example for the software tool "StS-Filter, 2016" is described in details. Some generalizations are mentioned.
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[+] About this article
Title
NORMAL PUGACHEV FILTERS FOR STATE LINEAR AUTOCORRELATED STOCHASTIC SYSTEMS
Journal
Systems and Means of Informatics
Volume 26, Issue 2, pp 63-78
Cover Date
2016-05-30
DOI
10.14357/08696527160204
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
autocorreled noise; 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", Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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