Prediction of aircraft surface trajectory based on the GRU-IKF model with attention mechanism

The prediction of aircraft taxiing trajectory helps to solve operational problems such as taxiing conflicts and long waiting times at airports, ensuring surface safety while improving service levels and increasing airport throughput. A model is proposed to predict the taxiing trajectory of a surface aircraft based on an attention mechanism that combines gated recurrent units (GRU) and an improved Kalman filter algorithm (IKF). This addresses the current situation where the performance of machine learning models depends on good data sets. In order to better extract data discrepancy features and learn input-to-output mapping relationships, three independent networks of gated recurrent units are first used to capture the future moment motion states and temporal dependencies of the aircraft. An enhanced extended Kalman filter is then fused with the neural network outputs to integrate them into the state prediction and update process, ultimately improving the predicted trajectory sequence accuracy. Finally, the validity of the model was verified using real aircraft taxi trajectories at Lukou Airport. The simulation results show that the proposed model can effectively and accurately predict aircraft taxi trajectories at the field with an overall mean square error of approximately 0.00128. Compared with the single recurrent neural network (RNN), long and short-term memory network (LSTM) and GRU model, the root mean square error (RMSE) is reduced by 72.9%, 54.7% and 39.9% respectively, and the prediction time is 40 ms, which could accurately and quickly predict the taxiing trajectory and provide assistance in reducing the operating load of the airport surface management system.