基于LSTM-ARIMA模型的短期航班飞行轨迹预测

Short-term Flight Trajectory Prediction Based on LSTM-ARIMA Model

高效精确的航班飞行轨迹预测是未来空中交通管理系统的关键技术之一,其旨在提高空中交通的运行能力和可预测性。针对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,构建了新的特征维度,分析了经度、纬度和高度三维数据的统计特性,将长短期记忆网络(Long Short-Term Memory,LSTM)对非线性和非平稳时间序列有较强的逼近能力,而差分自回归移动平均模型(Autoregressive Integrated Moving Average,ARIMA)对线性时间序列的处理能力更优的特点相结合,提出了一种以LSTM为主ARIMA为辅的组合短期航迹预测模型,先利用LSTM作为主预测模型对经纬度和高度进行预测,再利用辅模型ARIMA对高度的线性关系进行建模,最后采用CRITIC方法将LSTM和ARIMA预测的高度值融合处理。实验结果表明,这种组合模型利用了两种模型的优势,提高了航迹预测的准确性。

The efficient and accurate trajectory prediction is the key technologies of future air traffic management system,which aims to improve the operational capability and the predictability of air traffic.Aiming at the problems of insufficient accuracy and instability of existing prediction methods,some new dimension features are constructed based on the existing historical trajectory data.By analyzing the statistical characteristics of three-dimensional data of longitude,latitude and height,a combined prediction model based on LSTM and ARIMA is proposed,which combines LSTM network’s strong approximation ability to non-linear and non-stationary time series and ARIMA’s better prediction performance in linear time series.Firstly,LSTM is used as the main prediction model to predict longitude,latitude and height,and then ARIMA is used to model linear relationship of height.Finally,CRITIC method is used to fuse the height values of LSTM and ARIMA prediction.The experimental results show that the combined model takes advantages of two models and improves the accuracy of trajectory prediction.