关键词: Huber方法, 高斯滤波, 高阶容积准则, 鲁棒性, 滤波精度

Abstract:  To further improve the filtering accuracy and robustness of high-degree cubature Kalman filter when the random variable within non-Gaussian distribution, this paper presents a new filtering algorithm named Huber-based robust high-degree cubature Kalman filter algorithm. It is interpreted that the basic idea of Huber method acting on Kalman filter can be described as truncating the average from the perspective of recursive Bayesian approximation estimation. The observation vector is preprocessed by Huber method within the framework of the existed filtering, then the normal measurement update is implemented by using the preprocessed observation information, the robustness of the HCKF algorithm is realized consequently. The new method without approximating nonlinear measurements model by using the statistical linear regression model, the preponderance of high-degree cubature transform is fully used and the high precision is maintained while the robustness is ensured. Simulations in the context of univariate non-stationary growth model and the problem of reentry vehicle tracking demonstrate that the new method has superior performance in robustness and efficiency.

Key words: Huber method, Gaussian filter, high-degree cubature rule, robustness, filtering accuracy