Hybrid 4-Dimensional Trajectory Prediction Model, Based on the Reconstruction of Prediction Time Span for Aircraft en Route

Abstract

This paper presents the results from a test of the performance of several general trajectory prediction methods and proposes a hybrid trajectory prediction model that aims to increase the safety of flights en route and improve airspace management capabilities by predicting the aircraft’s four-dimensional trajectory (4DT) more accurately. The automatic dependent surveillance-broadcast (ADS-B) data from 589 trajectories of cruising aircraft from the Guangzhou area were extracted for experiments. Numerous trajectory prediction methods, including velocity trend extrapolation, long short-term memory (LSTM), stateful-LSTM, back propagation (BP) neural network, a one-dimensional convolutional neural network (1D-ConvNet), Kalman filter, and flight plan interpolation were used for prediction experiments, and their performance at different time spans of prediction is obtained. By extracting the best methods using different time spans of prediction, a hybrid prediction model is proposed based on the reconstruction of these methods. For the data in this paper, the mean squared error (MSE) of the hybrid prediction model is significantly reduced compared to other methods in different time spans of prediction, which has great significance for future trajectory prediction in a structured airspace.