Nonlinear Maximum Correntropy UKF Algorithm for GNSS/SINS Integrated Navigation System

Objectives: Unscented Kalman filter (UKF) is an effective method to solve the nonlinear filtering of GNSS/SINS integrated navigation system. UKF shows good performance in Gaussian noise, but its performance will be seriously degraded in non-Gaussian noise, especially when the system is interfered by some heavy tail pulse noise. In order to improve the robustness of UKF against heavy tail pulse noise, this paper proposed a maximum correntropy UKF (MCUKF) algorithm for GNSS/SINS integrated navigation system is proposed in this paper. Methods: Firstly, the nonlinear system model of GNSS/SINS integrated navigation system is established, which is characterized by nonlinear state equation and linear measurement variance. Then, a prior estimation of the state and its covariance matrix is obtained by using the unscented transform (UT) of UKF. Finally, the maximum correntropy criterion and statistical linear regression model are used to obtain the posterior estimation of the state and its covariance matrix, and the fixed point iteration of MCUKF algorithm is designed. Results: The simulation results show that the performance of UKF is slightly better than MCUKF in Gaussian noise; Compared with UKF, MCUKF with a core bandwidth of 5 can improve position accuracy by 13.4% and velocity accuracy by 10.3%; Compared with AKF, MCUKF can improve position accuracy by 8.8% and speed accuracy by 7.5%. Conclusions: The experimental results show that the filtering performance of MCUKF is obviously better than that of UKF at small core bandwidth, which can improve the filtering accuracy of integrated navigation system in complex environment.