Filtering of Differential Nonlinear Systems via a Carleman Approximation Approach; 44th IEEE Conf. on Decision and Control & European Control Conference (CDC-ECC 2005)

Author

Germani, Alfredo ; Manes, Costanzo ; Palumbo, Pasquale

Author_Institution

Dipartimento di Ingegneria Elettrica, Universitá degli Studi dell´´Aquila, Poggio di Roio, 67040 L´´Aquila, Italy, germani@ing.univaq.it

fYear

2005

fDate

12-15 Dec. 2005

Firstpage

5917

Lastpage

5922

Abstract

This paper deals with the state estimation problem for a stochastic nonlinear differential system driven by a standard Wiener process. The solution here proposed is a linear filtering algorithm and is achieved by means of the Carleman approximation scheme applied to both the state and the measurement nonlinear equations. Such a procedure allows to define an approximate representation by means of a suitable bilinear system for which a filtering algorithm is available from literature. Numerical simulations support the theoretical results and show a rather interesting improvement in terms of sampled error covariance of the proposed approach with respect to the classical Kalman-Bucy filter applied to the linearized differential system.

Keywords

Carleman approximation; Extended Kalman Filter; Nonlinear filtering; Stochastic Systems; Approximation algorithms; Control systems; Filtering algorithms; Maximum likelihood detection; Nonlinear control systems; Nonlinear equations; Nonlinear filters; Nonlinear systems; State estimation; Stochastic systems; Carleman approximation; Extended Kalman Filter; Nonlinear filtering; Stochastic Systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on

Print_ISBN

0-7803-9567-0

Type

conf

DOI

10.1109/CDC.2005.1583108

Filename

1583108