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An Iterative Ensemble Kalman Filter
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AI
DOI：10.1109/TAC.2011.2154430.png)
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Abstract
En 中文
The ensemble Kalman filter is a Monte Carlo method for state estimation of nonlinear models, developed as an alternative or improvement of the extended Kalman filter. In this technical note we introduce an iterative extension to the ensemble Kalman filter. Iterations are introduced to improve the estimates in the cases where the relationship between the model and observations is not linear. The iterations converge, but to a solution where the data are overfitted. An essential stopping criteria is therefore introduced for the proposed method. We show that the iterative ensemble Kalman filter gives improvements compared to the standard ensemble Kalman filter. The filter is also compared to an already existing iterative version of the ensemble Kalman filter, and differences are discussed.
Keywords:
Ensemble Kalman filter (EnKF)
Kalman filter
probability density function (PDF)
sequential importance resampling (SIR) filter
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
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