@inproceedings{7aebe8b52ce84b16bb00afa27a59b257,
title = "Asymptotic convergence of the ensemble kalman filter",
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 filtering, Multidimensional signal processing, Recursive estimation, Remote sensing",
author = "Butala, {Mark D.} and Jonghyun Yun and Yuguo Chen and Frazin, {Richard A.} and Farzad Kamalabadi",
year = "2008",
doi = "10.1109/ICIP.2008.4711882",
language = "English (US)",
isbn = "1424417643",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "825--828",
booktitle = "2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings",
note = "2008 IEEE International Conference on Image Processing, ICIP 2008 ; Conference date: 12-10-2008 Through 15-10-2008",
}