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Abstract
In this paper we consider the problem of state estimation of a dynamic system whose evolution is described by a nonlinear continuous-time stochastic model. We also assume that the system is observed by a sensor in discrete-time moments. To perform state estimation using uncertain discrete-time data, the system model needs to be discretized. We compare two methods of discretization. The first method uses the classical forward Euler method. The second method is based on the continuous-time simulation of the deterministic part of the nonlinear system between consecutive times of measurement. For state estimation we apply an unscented Kalman Filter, which - as opposed to the well known Extended Kalman Filter - does not require calculation of the Jacobi matrix of the nonlinear transformation associated with this method.
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- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Title of issue:
- Advanced and Intelligent Computations in Diagnosis and Control strony 91 - 104
- ISSN:
- 2194-5357
- Language:
- English
- Publication year:
- 2016
- Bibliographic description:
- Domżalski M., Kowalczuk Z.: Discrete-time estimation of nonlinear continuous-time stochastic systems// Advanced and Intelligent Computations in Diagnosis and Control/ ed. Z. Kowalczuk Cham – Heidelberg – New York – Dordrecht – London : Springer IP Switzerland, 2016, s.91-104
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-23180-8_7
- Verified by:
- Gdańsk University of Technology
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