  This article introduces a new algorithm for nonlinear state estimation based
on deterministic sigma point and EKF linearized framework for priori mean and
covariance respectively. This method reduces the computation cost of UKF about
50% and has better accuracy compared to EKF due to propagating mean and
Covariance of state to 3rd order Taylor series. Several types of Kalman filter
have been presented before to reduce the computation cost of UKF, however, this
new KF is a better choice because of its simplicity, numerical stability, and
accuracy for real-time implementation. Examples verify the effectiveness of the
proposed method.
