Minimum Error Entropy Kalman Filter

To date, most linear and nonlinear Kalman filters (KFs) have been developed under the Gaussian assumption well-known minimum mean square error (MMSE) criterion. In order to improve robustness with respect impulsive (or heavy-tailed) non-Gaussian noises, maximum correntropy criterion (MCC) has recently used replace MMSE in developing several robust Kalman-type filters. deal more complicated noises such as from multimodal distributions, this article, we develop a new filter, called entropy KF (MEE-KF), by using (MEE) instead of or MCC. Similar MCC-based KFs, proposed filter is also an online algorithm recursive process, which propagation equations are give prior estimates state covariance matrix, fixed-point update posterior estimates. addition, MEE extended (MEE-EKF) for performance improvement situations. The high accuracy strong MEE-KF MEE-EKF confirmed experimental results.