DocumentCode :
1864369
Title :
Asymptotic convergence of the ensemble Kalman filter
Author :
Butala, Mark D. ; Yun, Jonghyun ; Chen, Yuguo ; Frazin, Richard A. ; Kamalabadi, Farzad
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL
fYear :
2008
fDate :
12-15 Oct. 2008
Firstpage :
825
Lastpage :
828
Abstract :
This paper formally addresses the asymptotic convergence of the ensemble Kalman filter (EnKF), a state estimation procedure that, when combined with a technique called localization, provides computationally tractable solutions to large-dimensional state estimation problems. The proof presented in this paper shows that the estimates given by the EnKF converge to the optimal estimates given by the Kalman filter (KF) and provides a formal justification for the use of the EnKF in dynamic remote sensing image formation. The implications of the proof are twofold: it shows that the EnKF converges to a well-defined limit and provides a formal argument that the EnKF is in fact a Monte Carlo algorithm that converges to the KF.
Keywords :
Kalman filters; Monte Carlo methods; remote sensing; state estimation; Monte Carlo algorithm; asymptotic convergence; computationally tractable solutions; dynamic remote sensing; ensemble Kalman filter; formal justification; image formation; large-dimensional state estimation; localization technique; Convergence; Geophysical measurements; Image converters; Monte Carlo methods; Recursive estimation; Remote sensing; Sea measurements; State estimation; Statistics; Stochastic processes; Kalman filtering; multidimensional signal processing; recursive estimation; remote sensing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on
Conference_Location :
San Diego, CA
ISSN :
1522-4880
Print_ISBN :
978-1-4244-1765-0
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2008.4711882
Filename :
4711882
Link To Document :
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