Algorithm of Gaussian Sum Based Cubature Kalman Filter for Non-Gaussian Systems

Abstract

To improve the performance of the cubature kalman filter (CKF) in nonlinear non-Gaussian filtering system, the Gaussian sum cubature kalman filter (GSCKF) is proposed. A formulation of the CKF for the nonlinear system with non-Gaussian process noise and non-Gaussian measurement noise is presented. The GSCKF uses the Gaussian sum theory to divide the non-Gaussian system into several Gaussian sub-systems. And each subsystem is conducted the filtering process by the CKF. A target tracking problem with non-Gaussian noise is used as a simulation application to compare the filtering performance of the GSCKF. The simulation results show that the GSCKF outperforms the unscented Kalman filter (UKF) and CKF under non-Gaussian conditions. It proves that for the nonlinear systems with non-Gaussian noise, the filtering performance of the GSCKF is significantly improved.