电讯技术2026，Vol.66Issue(3)：350-359,10.DOI:10.20079/j.issn.1001-893x.241111001
一种基于Transformer-TCN-GRU的未来空管4D航迹预测方法
A Transformer-TCN-GRU-based Method for 4D Trajectory Prediction in Future Air Traffic Management
摘要
Abstract
To meet the stringent requirements for high-precision trajectory prediction under trajectory-based operations(TBO),a 4D trajectory prediction model based on a Transformer-TCN-GRU architecture is proposed.The model integrates the self-attention mechanism of Transformers,the multi-scale feature extraction capabilities of Temporal Convolutional Networks(TCN),and the long-short term dependency modeling advantages of Gated Recurrent Units(GRU)to achieve high-precision predictions for complex trajectory data.In terms of data processing,interpolation and normalization preprocessing is applied to terminal area trajectory data collected by the Automatic Dependent Surveillance-Broadcast(ADS-B)system to ensure data continuity and stability.During model training,Bayesian optimization is employed to fine-tune hyperparameters,further enhancing both prediction accuracy and training efficiency.Experimental results demonstrate that the proposed Transformer-TCN-GRU model significantly outperforms traditional long short-term memory(LSTM),GRU,and TCN-GRU models in terms of prediction accuracy and robustness especially in regions exhibiting drastic changes in flight states.关键词
空中交通管理/4D航迹预测/基于航迹的运行(TBO)/ADS-B数据/自注意力机制Key words
air traffic management/4D trajectory prediction/trajectory-based operations(TBO)/ADS-B data/self-attention mechanism分类
航空航天引用本文复制引用
孔建国,李龙超,梁海军,黄宇杰..一种基于Transformer-TCN-GRU的未来空管4D航迹预测方法[J].电讯技术,2026,66(3):350-359,10.基金项目
中央高校基本科研业务费资助项目(PHD2023-035,25CAFUC10040)) （PHD2023-035,25CAFUC10040）
