基于改进自适应无迹卡尔曼滤波的国产民机导航数据滤波算法

针对国产民用飞机导航数据存在杂波不能准确测量的问题，提出一种基于改进自适应无迹卡尔曼滤波(adaptive unscented Kalman filter，AUKF)算法的导航数据滤波方法。将无迹卡尔曼滤波(unscented Kalman filter，UKF)与改进Sage-Husa次优无偏极大后验噪声估计器结合构造出改进AUKF，有效解决了在模型不确定或干扰信号统计特性不完全得知的情况下，滤波精度低甚至发散的问题，同时与维纳滤波器和小波阈值法滤波效果进行对比。选择ARJ21飞机实际运行的高度、经度及纬度数据进行仿真，结果表明：改进后的AUKF算法较其他滤波算法精度更高，有效提高了导航数据的可靠性。研究对提高国产民机导航定位精度具有重要意义。

Research on Domestic Civil Aircraft Navigation Data Filtering Algorithm Based on Improved Adaptive Unscented Kalman Filter

Aiming at the problem that the domestic civil aircraft navigation data can not be accurately measured due to clutter, a navigation data filtering method based on improved adaptive unscented Kalman filter (AUKF) algorithm is proposed. The improved AUKF is constructed by combining unscented Kalman filter (UKF) with improved sage-Husa sub optimal unbiased maximum posterior noise estimator. The problem of low accuracy and divergence is effectively solved in the case of uncertain model or incomplete knowledge of statistical characteristics of interference signals. At the same time, the filter effect is compared with Wiener filter and wavelet threshold method. The simulation is carried out with the longitude, latitude and altitude data of domestic ARJ21. The results show that the filtering accuracy of the improved AUKF algorithm is higher than others, and effectively improves the reliability of navigation data. The research is of great significance to improve the navigation and positioning accuracy of domestic civil aircraft.