DocumentCode :
700472
Title :
Two-stage estimator for dynamic stochastic systems subject to unknown inputs and random bias
Author :
Keller, J.Y. ; Darouach, M.
Author_Institution :
CRAN, Univ. de Nancy I, Cosnes-et-Romain, France
fYear :
1997
fDate :
1-7 July 1997
Firstpage :
255
Lastpage :
260
Abstract :
The purpose of this paper is to give an optimal solution of a two-stage estimator for discrete-time stochastic systems subject to unknown inputs, random biases or any disturbances evolving in accordance with a dynamic state equation. The proposed two-stage Kalman filter is based on the use of the maximum likelihood descriptor Kalman filter developed by Nikoukhah et al. applied here for state estimation of dynamic systems subject to unknown inputs. Necessary and sufficient conditions for convergence and stability of the proposed two-stage Kalman estimator are established.
Keywords :
Kalman filters; convergence; discrete time systems; maximum likelihood estimation; stability; state estimation; stochastic systems; convergence; discrete-time stochastic systems; disturbances; dynamic state equation; dynamic stochastic systems; maximum likelihood descriptor; necessary conditions; optimal solution; random bias; stability; state estimation; sufficient conditions; two-stage Kalman estimator; two-stage Kalman filter; unknown inputs; Convergence; Estimation; Kalman filters; Mathematical model; Noise; Stochastic systems; Sufficient conditions; Decentralized; Estimation; Observers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (ECC), 1997 European
Conference_Location :
Brussels
Print_ISBN :
978-3-9524269-0-6
Type :
conf
Filename :
7082102
Link To Document :
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