Abstarct: In a GPS/INS navigation system, the extended Kalman filter (EKF) is widely used to estimate the navigation states. However, this filter is a first order approximation to the nonlinear system and when the nonlinearity of the system is high, the estimation accuracy degrades. Bayesian filtering refers to the process of sequentially estimating the current state of a complex dynamic system from noisy partial measurements using Bayes' rule. Cubature Kalman filter is a new approximate Bayesian filter for discrete-time nonlinear filtering problem. To develop this filter, it is assumed that the predictive density of the joint state-measurement random variable is Gaussian. In this way, the optimal Bayesian filter reduces to the problem of how to compute various multi-dimensional Gaussian-weighted moment integrals. To numerically compute these integrals, a third-degree spherical-radial cubature rule is proposed. This cubature rule entails a set of cubature points scaling linearly with the state-vector dimension. The cubature Kalman filter therefore provides an efficient solution even for high-dimensional nonlinear filtering problems. In this thesis, nonlinear filtering method Cubature Kalman filter (CKF) is analysed through Taylor expansion and the CKF's capability in capturing higher-order terms of nonlinear system is shown. Then considering a nonlinear attitude exxpression, performance comparison between these two filters is analysed baxsed on the degree of observability of the attitude states.