Ensemble Kalman filtering for non-linear likelihood models using kernel-shrinkage regression techniques

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.