Do the Contemporary Cubature and Unscented Kalman Filtering Methods Outperform Always the Traditional Extended Kalman Filter? (1604.04498v1)

Abstract: This brief technical note elaborates three well-known state estimators, which are used extensively in practice. These are the rather old-fashioned extended Kalman filter (EKF) and the recently-designed cubature Kalman filtering (CKF) and unscented Kalman filtering (UKF) algorithms. Nowadays, it is commonly accepted that the contemporary techniques outperform always the traditional EKF in the accuracy of state estimation because of the higher-order approximation of the mean of propagated Gaussian density in the time- and measurement-update steps of the listed filters. However, the present paper specifies this commonly accepted opinion and shows that despite the mentioned theoretical fact the EKF may outperform the CKF and UKF methods in the accuracy of state estimation when the stochastic system under consideration exposes a stiff behavior. That is why stiff stochastic models are difficult to deal with and require effective state estimation techniques to be designed yet.