Abstract: 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.

Key words: Flight trajectory prediction, Self-supervised learning, Self-attention, Deep learning