Short-term 4D Trajectory Prediction Algorithm Based on Online-updating LSTM Network

Some factors in the flight process will have an impact on the current trajectory. There is a certain difference between the real-time trajectory and the historical trajectory, and the prediction performance of the trajectory prediction model based on historical trajectory data becomes worse. To solve this problem, a short-term 4D trajectory prediction algorithm based on online-updating long short-term memory (LSTM) is proposed. The prediction algorithm is composed of two parts: the initial parameters training of the prediction model based on historical trajectory data and the parameters online-updating for the prediction model based on real-time trajectory data. The trajectory prediction model is established through the LSTM neural network first. The historical trajectory data are used to train the model and the trained parameters of the model are saved. Then, the real-time trajectory data are used to retrain and fine-tune the parameters of the trajectory prediction model. The online-updating prediction model is used to predict the short-term 4D trajectory data, so as to achieve the purpose of improving the prediction accuracy. The actual trajectory data are used to verify the performance of the algorithm. Experimental results show that the new prediction model with a good generalization ability can take into account the influence of various factors on the trajectory during the real-time flight process and improve the prediction accuracy of longitude, latitude, height, and time effectively.