Title :
Data-driven method for Kalman filtering
Author :
Xie, Wen ; Xia, Yuanqing
Author_Institution :
Sch. of Autom., Beijing Inst. of Technol., Beijing, China
Abstract :
In this paper, the state estimation problem is considered based on the input-output data. A data-driven subspace identification method combined with the Kalman on-line filtering algorithm is proposed for solving the state estimation problem for a class of dynamical systems where the exact models can not be established. Simulation results are further presented to show the effectiveness of the proposed strategy.
Keywords :
Kalman filters; nonlinear dynamical systems; state estimation; Kalman on-line filtering; data-driven subspace identification; dynamical systems; input-output data; state estimation; Computational modeling; Equations; Kalman filters; Mathematical model; Observability; Prediction algorithms; State estimation;
Conference_Titel :
Intelligent Control and Information Processing (ICICIP), 2011 2nd International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4577-0813-8
DOI :
10.1109/ICICIP.2011.6008364
