Robust Adaptive Gaussian Mixture Sigma Point Particle Filter

This paper presents a new robust adaptive Gaussian mixture sigma-point particle filter by adopting the concept of robust adaptive estimation to the Gaussian mixture sigma-point particle filter. This method approximates state mean and covariance via Sigma-point transformation combined with new available measurement information. It enables the estimations of state mean and covariance to be adjusted via the equivalent weight function and adaptive factor, thus restraining the disturbances of singular measurements and kinematic model noise. It can also obtain efficient predict prior and posterior density functions via Gaussian mixture approximation to improve the filtering accuracy for nonlinear and non-Gaussian systems. Simulation results and comparison analysis demonstrate the proposed method can effectively restrain the disturbances of abnormal measurements and kinematic model noise on state estimate, leading to improved estimation accuracy.