Sigma point transformation for Gaussian mixture distributions applied to attitude estimation

This paper describes the development of an approximate method for propagating uncertainty through stochastic dynamical systems using a quadrature rule integration based method. The development of quadrature rules for Gaussian mixture distributions is discussed. A numerical solution to this problem is considered that uses a Gram-Schmidt process. The new approach is applied to the attitude estimation problem. The proposed method outperforms the unscented Kalman filter for attitude estimation for scenario with large initial error.