http://www.cnr.it/ontology/cnr/individuo/prodotto/ID7329
Filtering of stochastic nonlinear differential systems via a Carleman approximation approach (Articolo in rivista)
- Type
- Label
- Filtering of stochastic nonlinear differential systems via a Carleman approximation approach (Articolo in rivista) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/TAC.2007.908347 (literal)
- Alternative label
Germani, A.; Manes, C.; Palumbo, P. (2007)
Filtering of stochastic nonlinear differential systems via a Carleman approximation approach
in IEEE transactions on automatic control (Print)
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Germani, A.; Manes, C.; Palumbo, P. (literal)
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- http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4380503&tag=1 (literal)
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- A. Germani, C. Manes: Dipartimento di Ingegneria Elettrica e dell'Informazione, Università degli Studi dell'Aquila, 67040 L'Aquila, Italy (literal)
- Titolo
- Filtering of stochastic nonlinear differential systems via a Carleman approximation approach (literal)
- Abstract
- This paper deals with the state estimation problem for stochastic nonlinear differential systems, driven by standard Wiener processes, and presents a filter that is a generalization of the classical Extended Kalman-Bucy filter (EKBF). While the EKBF is designed on the basis of a first order approximation of the system around the current estimate, the proposed filter exploits a Carleman-like approximation of a chosen degree v >= 1. The approximation procedure, applied to both the state and the measurement equations, allows to define an approximate representation of the system by means of a bilinear system, for which a filtering algorithm is available from the literature. Numerical simulations on an example show the improvement, in terms of sample error covariance, of the filter based on the first-order, second-order and third-order system approximations (v = 1, 2, 3). (literal)
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