Asymptotic convergence of the ensemble Kalman filter
Butala, M.D.
Jonghyun Yun
Yuguo Chen
Frazin, R.A.
Kamalabadi, F.
Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL;
This paper appears in: Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on
Publication Date: 12-15 Oct. 2008
On page(s): 825-828
Location: San Diego, CA,
ISSN: 1522-4880
ISBN: 978-1-4244-1765-0
INSPEC Accession Number: 10422906
Digital Object Identifier: 10.1109/ICIP.2008.4711882
Current Version Published: 2008-12-12
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.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
