6D flight trajectory prediction in scenarios with frequent scene changes near the airport
The increasing volume of aviation activities poses challenges to air traffic control,with trajectory prediction technology playing a pivotal role in ensuring the safety and orderliness of airspace traffic.The heightened density of flights near airports presents difficulties for trajectory prediction.A hybrid neural network model based on Attention-CNNs,bidirectional long short-term memory(LSTM),and XGBoost is proposed using data from the automatic dependent surveillance-broadcast(ADS-B)system.This model is designed to forecast 6D information pertaining to flight trajectories,including time,longitude,latitude,altitude,velocity,and heading angle.A trajectory dataset composed of spatial-temporal information and flight dynamics data is used to validate the efficiency of our approach.Quantitative analysis reveals that the proposed model outperforms comparative models in terms of evaluation metrics.This method offers an effective solution for ensuring the safe operation of aviation management systems in airport environments.
air traffic managementtrajectory predictionCNNbidirectional long short-term memory(BiLSTM)attention mechanism
万方数据
维普
