Recursive nonlinear filter for a continuous discrete-time model: separation of parameters and observations

Author

Lototsky, Sergey V. ; Rozovskii, Boris L.

Author_Institution

Dept. of Math., MIT, Cambridge, MA, USA

Volume

43

Issue

8

fYear

1998

fDate

8/1/1998 12:00:00 AM

Firstpage

1154

Lastpage

1158

Abstract

A new nonlinear filtering algorithm is proposed for the model where the state is a randomly perturbed nonlinear dynamical system and the measurements are made at discrete-time moments in Gaussian noise. It is shown that the approximate scheme based on the algorithm converges to the optimal filter, and the error of the approximation is computed. The algorithm makes it possible to shift offline the most time-consuming operations related to solving the Fokker-Planck equations and computing the integrals with respect to the filtering density

Keywords

Gaussian noise; discrete time systems; filtering theory; nonlinear dynamical systems; nonlinear filters; observers; random processes; recursive filters; Fokker-Planck equations; Gaussian noise; continuous discrete-time model; filtering density; observations; offline shifting; optimal filter; parameters; randomly perturbed nonlinear dynamical system; recursive nonlinear filter; Density measurement; Filtering algorithms; Gaussian noise; Integral equations; Maximum likelihood estimation; Noise measurement; Nonlinear dynamical systems; Nonlinear equations; Nonlinear filters; State estimation;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.704992

Filename

704992