This article is cited in 3 scientific papers (total in 3 papers)
Normal Pugachev filters for state linear 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 119333,
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.
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).
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I. N. Sinitsyn, E. R. Korepanov, “Normal Pugachev filters for state linear stochastic systems”, Inform. Primen., 9:2 (2015), 30–38
Citation in format AMSBIB
\by I.~N.~Sinitsyn, E.~R.~Korepanov
\paper Normal Pugachev filters for state linear stochastic systems
\jour Inform. Primen.
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This publication is cited in the following articles:
I. N. Sinitsyn, E. R. Korepanov, “Normalnye filtry Pugacheva dlya avtokorrelirovannykh stokhasticheskikh sistem, lineinykh otnositelno sostoyaniya”, Sistemy i sredstva inform., 26:2 (2016), 63–78
I. N. Sinitsyn, E. R. Korepanov, “Normalnye uslovno-optimalnye filtry i ekstrapolyatory Pugacheva dlya stokhasticheskikh sistem, lineinykh otnositelno sostoyaniya”, Inform. i ee primen., 10:2 (2016), 14–23
I. N. Sinitsyn, V. I. Sinitsyn, E. R. Korepanov, “Extending the theory of Liptser–Shiryaev filters”, Autom. Remote Control, 81:4 (2020), 602–613
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