Self-supervised Flight Trajectory Prediction Based on Data Augmentation

• Published in: Ji suan ji ke xue Vol. 50; no. 2; pp. 130 - 137
• Main Authors: Wang, Pengyu, Tai, Wenxin, Liu, Fang, Zhong, Ting, Luo, Xucheng, Zhou, Fan
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.02.2023; Editorial office of Computer Science
• Subjects: Air traffic management; Aircraft; Data augmentation; Datasets; Distillation; flight trajectory prediction|self-supervised learning|self-attention|deep learning; Machine learning; Management systems; Recurrent neural networks; Regularization; System effectiveness; Trajectories
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.211200016

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 lea