Gaussian sum high-order CKF for dynamic state estimation

Abstract

The problem of state estimation in nonlinear/non-Gaussian systems has generated significant interest in the literature. A new version of Gaussian sum estimation algorithm based on the high-degree cubature Kalman filter (HCKF) is proposed to further improve accuracy and stability of the traditional Gaussian sum filter. The proposed GS-HCKF approximates the predicted and posterior densities as a finite number of weighted sums of Gaussian densities. It is corroborated in the theoretical analysis and the simulation that the proposed GS-HCKF has integrated advantages with respect to computational accuracy and time complexity for nonlinear non-Gaussian filtering problems.