Suboptimal discrete filters for stochastic systems with different types of observations

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In [1], we developed a new suboptimal filtering methods for a class of linear and nonlinear continuous dynamic systems with multidimensional observation vector. The methods are based on the decomposition of Kalman filtering and extended Kalman filtering equations by observation vector. In this paper, we present a generalization of these filtering methods to discrete stochastic systems determined by difference equations. The obtained filtering equations have a parallel structure and are very suitable for parallel programming. Example demonstrating the efficiency of the proposed suboptimal filters is given.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
1998
Language
English
Article Type
Article
Citation

COMPUTERS & MATHEMATICS WITH APPLICATIONS, v.35, no.3, pp.17 - 27

ISSN
0898-1221
DOI
10.1016/S0898-1221(97)00275-7
URI
http://hdl.handle.net/10203/73840
Appears in Collection
MA-Journal Papers(저널논문)
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