Asymptotic study of estimation in filtering for linear systems with jump parameters

Author

Dufour, F. ; Elliott, R.J. ; Tsoi, A.

Author_Institution

Lab. des Signaux et Syst., CNRS, Gif-sur-Yvette, France

Volume

4

fYear

1995

fDate

13-15 Dec 1995

Firstpage

3349

Abstract

In this work, we study the nonlinear filtering problem for a linear diffusion process X, the coefficients of which are fed by a Markovian jump process Z. The state process (Z,X) is assumed to be observed with additive observation noise of order ε. We derive approximate finite dimensional filters which are solutions of stochastic differential equations driven by the observation process; they are asymptotically efficient as ε→0. Upper bounds for the corresponding errors are given

Keywords

Markov processes; differential equations; diffusion; filtering theory; linear systems; parameter estimation; stochastic systems; Markovian jump process; additive observation noise; finite dimensional filters; jump parameters; linear systems; nonlinear filtering; state process; stochastic differential equations; upper bounds; Additive noise; Differential equations; Diffusion processes; Eigenvalues and eigenfunctions; Filtering; Linear systems; Mathematics; Nonlinear filters; Stochastic resonance; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1995., Proceedings of the 34th IEEE Conference on

Conference_Location

New Orleans, LA

ISSN

0191-2216

Print_ISBN

0-7803-2685-7

Type

conf

DOI

10.1109/CDC.1995.479004

Filename

479004