Optimal filtering for incompletely measured polynomial states over linear observations

In this paper, the optimal filtering problem for polynomial system states over linear observations with an arbitrary, not necessarily invertible, observation matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. A...

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Bibliographic Details
Published in:International journal of adaptive control and signal processing 2008-06, Vol.22 (5), p.482-494
Main Authors: Basin, Michael, Calderon-Alvarez, Dario, Skliar, Mikhail
Format: Article
Language:English
Subjects:
Errors
Estimates
Filtering
Filtration
Mathematical analysis
nonlinear polynomial system
Optimization
stochastic system
Transformations
Variance
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Summary:In this paper, the optimal filtering problem for polynomial system states over linear observations with an arbitrary, not necessarily invertible, observation matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the observation equation is introduced to reduce the original problem to the previously solved one with an invertible observation matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular case of a third‐order state equation. In the example, performance of the designed optimal filter is verified against a conventional extended Kalman–Bucy filter. Copyright © 2007 John Wiley & Sons, Ltd.
ISSN:0890-6327
1099-1115
DOI:10.1002/acs.1004