电讯技术2026，Vol.66Issue(1)：21-29,9.DOI:10.20079/j.issn.1001-893x.240819002
一种基于时域融合Transformer的4D航迹预测方法
A Method of 4D Trajectory Prediction Based on Temporal Fusion Transformer
摘要
Abstract
To address the limitations of traditional 4D trajectory prediction methods in data singularity and feature selection,a 4D trajectory prediction method based on the Temporal Fusion Transformer(TFT)model is proposel.The method introduces multiple features,such as descent rate and temporal components,and classifies the data based on whether it varies over time or is numerical,to reflect the differences at various stages of flight.The TFT model is employed to effectively capture the implicit correlations between features,thereby improving prediction accuracy.Additionally,quantile regression is combined to quantify uncertainty and provide trajectory predictions with confidence intervals.Experimental results show that the proposed method outperforms traditional models on real-world data:compared with that of the CNNLSTM model and the LSTM model,the mean distance error is reduced by 22.7%and 50.9%,respectively.The longitudinal,lateral,and vertical errors are 305.01 m,177.91 m,and 25.23 m,respectively.The results validate the effectiveness of the model in solving trajectory prediction problems and demonstrate it can provide valuable support for refined air traffic control.关键词
空中交通管制/4D航迹预测/自动相关监视系统数据/时域融合Transformer/时间序列预测Key words
air traffic control/4D trajectory prediction/automatic dependent surveillance-broadcast data/temporal fusion Transformer/time series prediction分类
航空航天引用本文复制引用
孔建国,马珂昕,梁海军,张向伟,常瀚文..一种基于时域融合Transformer的4D航迹预测方法[J].电讯技术,2026,66(1):21-29,9.基金项目
国家重点研发计划(2021YFF0603904) （2021YFF0603904）
中央高校基本科研业务费专项资金(PHD2023-035) （PHD2023-035）
2024年度四川省级大学生创新创业项目(S202410624084) （S202410624084）
