Asymptotic convergence of the ensemble kalman filter

Mark D. Butala, Jonghyun Yun, Yuguo Chen, Richard A. Frazin, Farzad Kamalabadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Pages825-828
Number of pages4
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: Oct 12 2008Oct 15 2008

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2008 IEEE International Conference on Image Processing, ICIP 2008
CountryUnited States
CitySan Diego, CA
Period10/12/0810/15/08

Keywords

  • Kalman filtering
  • Multidimensional signal processing
  • Recursive estimation
  • Remote sensing

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Butala, M. D., Yun, J., Chen, Y., Frazin, R. A., & Kamalabadi, F. (2008). Asymptotic convergence of the ensemble kalman filter. In 2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings (pp. 825-828). [4711882] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2008.4711882