Variational Bayesian-Based Adaptive Maximum Correntropy Generalized High-Degree Cubature Kalman Filter

In this paper, we propose a new filtering algorithm, the variational Bayesian-based adaptive maximum correntropy generalized high-degree cubature Kalman filter, which is designed to improve filtering accuracy under conditions of unknown measurement noise covariance and measurement outliers. Considering that the generalized high-degree cubature rule can solve the high dimensional nonlinear problem well, based on the generalized high-degree cubature Kalman filter, the variational Bayesian method is utilized to approximate the measurement noise covariance, and the maximum correntropy criterion is used to reduce the influence of the measurement outliers on the state estimation. Additionally, we introduce a resistance factor to correct measurement values and optimize the kernel bandwidth for different types of noise. Simulation experiments on target tracking and integrated navigation demonstrate that our proposed algorithm effectively suppresses unknown time-varying noise and non-Gaussian mutation noise, outperforming existing filtering algorithms in terms of estimation accuracy, robustness, and adaptability.