Informatics and Applications2016, Volume 10, Issue 1, pp 34-44
ORTHOGONAL SUPOPTIMAL FILTERS FOR NONLINEAR STOCHASTIC SYSTEMS ON MANIFOLDS
AbstractThe authors developed the synthesis theory of suboptimal filers (SOF) based on normal approximation method (NAM), statistical linearization method (SLM), orthogonal expansions method (OEM), and quasi-moment method (QMM) for nonlinear differential stochastic systems on manifolds (MStS) with Wiener and Poisson noises.
Exact optimal (for mean square error criteria) equations for MStS with Gaussian noises in observation equations for the one-dimensional a posteriori characteristic function are derived. Problems of approximate solving of exact equations are discussed. Accuracy and sensitivity equations are presented. A test example for the nonlinear scalar differential equation with additive and multiplicative noises is given. Some generalizations are mentioned.
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TitleORTHOGONAL SUPOPTIMAL FILTERS FOR NONLINEAR STOCHASTIC SYSTEMS ON MANIFOLDS
JournalInformatics and Applications
2016, Volume 10, Issue 1, pp 34-44
PublisherInstitute of Informatics Problems, Russian Academy of Sciences
Key wordsa posteriori one-dimensional distribution; coefficient of orthogonal expansion; first sensitivity function; normal approximation method; normal suboptimal filter; orthogonal expansion method; orthogonal suboptimal filter; quasi-moment method; quasi-moment; statistical linearization method; stochastic system on manifolds; suboptimal filter; Wiener white noise
AuthorsI. N. Sinitsyn
Author AffiliationsInstitute of Informatics Problems, Federal Research Center “Computer Sciences and Control” of the Russian Academy of Sciences, 44-2 Vavilov Str.,Moscow 119333, Russian Federation