基于数据增强的自监督飞行航迹预测

Self-supervised Flight Trajectory Prediction Based on Data Augmentation

准确的飞行航迹预测可以帮助空中交通管理系统对潜在的危险提出预警,并有效地为安全出行提供指导。飞机飞行所处的大气情况复杂多变,飞行航迹受大气扰动、空中云层等外部因素的影响很大,使得飞行航迹预测问题十分复杂和困难。另外,由于某些飞行区域所在的地面环境恶劣,无法部署足够的信号基站,而某些飞行区域的飞行信号由多个信号基站采集组合而成,造成最终得到的飞行航迹数据存在稀疏和含噪等问题,进一步增加了飞行航迹预测的难度。文中提出了一种基于数据增强的自监督飞行航迹学习方法。此方法采用基于正则化的数据增强方式,扩充了稀疏的航迹数据集并处理了数据中包含的异常值,利用最大化互信息的方式进行自监督预训练,以挖掘飞行航迹中蕴含的运动模式和航行意图,采用一种带有蒸馏机制的多头自注意力模型作为基础模型,解除了循环神经网络长期依赖和无法并行计算的限制,并利用注意力蒸馏机制和生成式解码方式降低了模型的复杂度,加快了其训练和预测的速度。在飞行航迹数据集上的评测结果显示,此方法较目前预测表现最优秀的方法在纬度、经度和高度上的预测结果的均方根误差各减少了20.8%,26.4%和25.6%,极大地提高了预测准确性。

Accurate flight trajectory predictions can help air traffic management systems make warnings for potential hazards and effectively provide guidance for safe travel.However,the atmospheric situation in which the planes flying is complicated and changeable.The flight track is affected by external factors such as atmospheric disturbance,the air cloud,making prediction difficult.In addition,due to the harsh ground environment where some flight areas are located,it is impossible to deploy enough signal base stations,while the flight signals in some flight areas are collected and combined by multiple signal base stations,resulting in sparse and noisy aircraft track data,which further increases the difficulty of flight track prediction.This paper proposes a technically enhanced self-supervision flight trajectory learning method.This method uses a regularization-based data enhancement mode to extend the sparse track data and process the abnormal values included in the dataset.It provides a self-supervised learning diagram by maximizing mutual information to dig the mobility pattern contained in the flight trajectory.The method employs a multi-head self-attention model with a distillation mechanism as a fundamental model to solve the long-term dependence problem of the recurrent neural network.In addition,the approach uses the distillation mechanism to reduce the complexity of the model and utilizes the generating decoding method to accelerate the speed of its training and prediction.The evaluation results on the flight trajectory dataset show that our method has a significant increase in the results of trajectory prediction compared with the state-of-the-art method that our approach reduces the root mean square error of the prediction results in latitude,longitude,and altitude by 20.8%,26.4%,and 25.6%,respectively.