Application of MCC-based Robust High-degree Cubature Kalman Filter in Integrated Navigation System

As the integrated navigation system is a nonlinear system, in the case of non-gaussian noise, the traditional nonlinear gaussian filtering algorithm has a serious problem of decreasing filtering precision. In this paper, a new robust high-degree Cubature Kalman filtering algorithm is proposed, which takes into account the nonlinearity of the system and non-gaussian noise. The algorithm improves the measurement updating process by using the Maximum correntropy criterion(MCC), and converts the traditional measurement updating problem into the linear regressione quation solving problem. Combines the advantages of Maximum correntropy criterion and Cubature Kalman filter to deal with non-Gaussian and nonlinear systems. The proposed algorithm is applied to the SINS/GPS integrated navigation system, the simulation results show that the proposed algorithm's filtering performance is greatly affected by the kernel width. Under the condition of gaussian mixture noise, the new robust high-degree Cubature Kalman filter based on Maximum correntropy criterion(MCC-HCKF) is more robust and has higher filtering precision than the traditional high-degree Cubature Kalman filter(HCKF).