随着状态维数的增加, UKF算法的计算量迅速增大, 且UKF算法对模型误差较敏感, 不适用于噪声为非高斯分布的系统模型。针对此问题, 在研究抗差估计、模型预测滤波和UKF的基础上, 提出了一种抗差模型预测Unscented卡尔曼滤波算法。该算法利用扩维方法将驱动噪声加入系统状态中, 增加了系统的状态信息, 采用模型预测滤波(MPF)抑制模型误差, 利用抗差估计增强系统的鲁棒性, 弥补了UKF算法对模型误差敏感的缺陷。将提出的算法应用于SINS/BNTS/CNS组合导航系统并进行仿真, 与自适应EKF和抗差自适应UKF算法比较, 结果表明, 提出的算法能有效抑制位置误差和速度误差, 滤波性能明显优于自适应EKF算法和抗差自适应UKF算法。

As the state dimension increases, the amount of calculation of Unscented Kalman Filter (UKF) algorithm increases rapidly. Besides, UKF is sensitive to the model error, and is not suitable for the system model whose noise doesn't subject to Gaussian distribution. In order to solve these problems, a new robust model-predictive UKF algorithm is proposed. This method incorporates the driving noise into the system state through the augmentation of state dimensions to add the system state information. The model error is restrained by Model Predictive Filter (MPF), and the robustness of the system is enhanced by the robust estimation. Thus the limitation of the traditional UKF algorithm which is sensitive to the model error is overcome. The proposed algorithm is applied to the new integrated SINS/BNTS/CNS navigation system for simulation, and is compared with the adaptive EKF algorithm and the robust adaptive UKF algorithm. The results show that the proposed algorithm can effectively restrain the attitude error and the velocity error, and its filtering capability is superior to that of the adaptive EKF and the robust adaptive UKF.