  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.
