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

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3 Author(s)
Germani, A. ; Dipartimento di Ingegneria Elettrica, Universitá degli Studi dell''Aquila, Poggio di Roio, 67040 L''Aquila, Italy, germani@ing.univaq.it ; Manes, C. ; Palumbo, P.

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.

Published in:

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

Date of Conference:

12-15 Dec. 2005