摘要
为了解决现有的航迹预测方法预测精度和稳定性不足的问题,在已有的历史航迹数据的基础上,提出一种基于无迹卡尔曼滤波的飞行航迹预测模型。根据已知航班的部分航迹点构建航空器运动观测方程和状态方程,并通过无迹卡尔曼滤波完成航迹预测,面向所得数据和原始数据展开误差比较分析,检验算法及预测过程准确性。研究结果表明,通过上述算法进行飞行航迹预测,不但精度较高,并且稳定性非常理想,与传统的扩展卡尔曼滤波算法相比较,具有较小的误差,为之后开展航空器航迹预测仿真识别工作提供一定的应用和借鉴参考。
In order to solve the problem of insufficient prediction accuracy and stability of the existing track prediction methods, a flight truck prediction model based on Unscented Kalman Filter was proposed based on existing historical track data. In this paper, the aircraft motion observation equation and state equation were constructed according to the known flight path points, and the track prediction was completed by Unscented Kalman Filter. The error comparison and analysis were carried out for the obtained data and the original data to verify the accuracy of the algorithm and prediction process. The research results show that the algorithm proposed in this paper not only has high accuracy, but also has ideal stability. Compared with the traditional Extended Kalman Filter algorithm, it has a small error, which provides a certain application and reference for future aircraft track prediction simulation and identification.
作者
陈明强
傅嘉赟
CHEN Ming-qiang;FU Jia-yun(Civil Aviation Flight University of China,Guanghan Sichuan 618307,China)
出处
《计算机仿真》
北大核心
2021年第6期27-30,36,共5页
Computer Simulation
关键词
航迹预测
无迹卡尔曼滤波
航迹精度
偏差分析
Track prediction
Unscented Kalman filter
Track accuracy
Deviation analysis
