In order to achieve higher accuracy in nonlinear/non-Gaussian state estimation, this paper proposes a new unscented Kalman filter (UKF). It uses a deterministic sampling approach. We choose the unscented transformation (UT) scaling parameters α=0.85, β=2, l=0 to construct 2n + 1 sigma points. These sigma points completely capture the mean and covariance of the Gaussian random variables of the nonlinear system Yi=F(Xi). Simulation results show that the posterior mean and covariance of the sigma points can achieve the accuracy of the third-order Taylor series expansion after having propagated through the true nonlinear system Yi=F(Xi). Extended Kalman filter (EKF) only can achieve the first-order accuracy. The computational complexity of UKF is the same level as that of EKF. UKF can yield better performance and higher accuracy than EKF.
