DocumentCode :
2293917
Title :
What is the ensemble Kalman filter and how well does it work?
Author :
Gillijns, S. ; Mendoza, O. Barrero ; Chandrasekar, J. ; De Moor, B.L.R. ; Bernstein, D.S. ; Ridley, A.
Author_Institution :
Katholieke Universiteit, Leuven
fYear :
2006
fDate :
14-16 June 2006
Abstract :
In this paper we described the ensemble Kalman filter algorithm. This approach to nonlinear Kalman filtering is a Monte Carlo procedure, which has been widely used in weather forecasting applications. Our goal was to apply the ensemble Kalman filter to representative examples to quantify the tradeoff between estimation accuracy and ensemble size. For all of the linear and nonlinear examples that we considered, the ensemble Kalman filter worked successfully once a threshold ensemble size was reached. In future work we will investigate the factors that determine this threshold value
Keywords :
Kalman filters; Monte Carlo methods; estimation theory; nonlinear filters; weather forecasting; Monte Carlo; ensemble Kalman filter algorithm; ensemble size; estimation accuracy; nonlinear Kalman filtering; weather forecasting; Covariance matrix; Gaussian noise; Jacobian matrices; Linear systems; Nonlinear dynamical systems; Nonlinear systems; Particle filters; Riccati equations; State estimation; Weather forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, 2006
Conference_Location :
Minneapolis, MN
Print_ISBN :
1-4244-0209-3
Electronic_ISBN :
1-4244-0209-3
Type :
conf
DOI :
10.1109/ACC.2006.1657419
Filename :
1657419
Link To Document :
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