I. N. Sinitsyn, E. R. Korepanov, “Normal Pugachev filters for state linear autocorrelated stochastic systems”, Sistemy i Sredstva Inform., 2016, Volume 26, Issue 2,Pages <nobr>63

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Normal Pugachev filters for state linear autocorrelated stochastic systems

I. N. Sinitsyn, E. R. Korepanov

Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119133, Russian Federation

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.

Keywords: 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).

Received: 25.02.2016

DOI: 10.14357/08696527160204