Cart (Loading....) | Create Account
Close category search window
 

The ensemble Kalman filter for combined state and parameter estimation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Evensen, G. ; StatoilHydro, Bergen

This article provides a fundamental theoretical basis for understanding EnKF and serves as a useful text for future users. Data assimilation and parameter-estimation problems are explained, and the concept of joint parameter and state estimation, which can be solved using ensemble methods, is presented. KF and EKF are briefly discussed before introducing and deriving EnKF. Similarities and differences between KF and EnKF are pointed out. The benefits of using EnKF with high-dimensional and highly nonlinear dynamical models are illustrated by examples. EnKF and EnKS are also derived from Bayes theorem, using a probabilistic approach. The derivation is based on the assumption that measurement errors are independent in time and the model represents a Markov process, which allows for Bayes theorem to be written in a recursive form, where measurements are processed sequentially in time. The practical implementation of the analysis scheme isdiscussed, and it is shown that it can be computed efficiently in the space spanned by the ensemble realizations. The square root scheme is discussed as an alternative method that avoids the perturbation of measurements. However, the square root scheme has other pitfalls, and it is recommended to use the symmetric square root with or without a random rotation. The random rotation introduces a stochastic component to the update, and the quality of the scheme may then not improve compared to the original stochastic EnKF scheme with perturbed measurements.

Published in:

Control Systems, IEEE  (Volume:29 ,  Issue: 3 )

Date of Publication:

June 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.