一种基于Transformer-TCN-GRU的未来空管4D航迹预测方法OA

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.