Recently there have been researches about new efficient nonlinear filtering techniques[1]~[3] in which the nonlinear filters generalize elegantly to nonlinear systems without the burdensome linearization steps. Thus, truncation errors due to linearization can be compensated. These filters include the unscented Kalman filter (UKF), the central difference filter (CDF) and the divided difference filter (DDF), and they are also called Sigma Point Filters (SPFs) in a unified way[4]. For higher order approximation of the nonlinear function. Ito and Xiong[6] introduced an algorithm called the Gauss Hermite Filter, which is revisited in [5]. The Gauss Hermite Filter gives better approximation at the expense of higher computation burden, although it’s less than the particle filter. The Gauss Hermite Filter is used as introduced in [5] with additional pruning step by adding threshold for the weights to reduce the quadrature points. Recently there have been researches about new efficient nonlinear filtering techniques [1] ~ [3] in which the nonlinear filters generalize elegantly to nonlinear systems without the burdensome linearization steps. Thus, truncation errors due to linearization can be compensated. These filters include the unscented Kalman filter (UKF), the central difference filter (CDF) and the divided difference filter (DDF), and they are also called Sigma Point Filters (SPFs) in a unified way [4]. For higher order approximation of the nonlinear function. The Gauss Hermite Filter gives a better approximation at the expense of higher computation burden, although it’s less than the particle filter. The Gauss Hermite Filter [6] introduced an algorithm called the Gauss Hermite Filter, which is revisited in [5] is used as introduced in [5] with additional pruning step by adding threshold for the weights to reduce the quadrature points.