Ensemble Kalman filtering for non-linear likelihood models using kernel-shrinkage regression techniques |
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| Authors: | Jon Sætrom Henning Omre |
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| Institution: | 1.Department of Mathematical Sciences,Norwegian University of Science and Technology,Trondheim,Norway |
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| Abstract: | One of the major limitations of the classical ensemble Kalman filter (EnKF) is the assumption of a linear relationship between
the state vector and the observed data. Thus, the classical EnKF algorithm can suffer from poor performance when considering
highly non-linear and non-Gaussian likelihood models. In this paper, we have formulated the EnKF based on kernel-shrinkage
regression techniques. This approach makes it possible to handle highly non-linear likelihood models efficiently. Moreover,
a solution to the pre-image problem, essential in previously suggested EnKF schemes based on kernel methods, is not required.
Testing the suggested procedure on a simple, illustrative problem with a non-linear likelihood model, we were able to obtain
good results when the classical EnKF failed. |
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