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)
Pagina inizio
  • 2166 (literal)
Pagina fine
  • 2172 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4380503&tag=1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 52 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • No.11 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 7 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 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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