Abstract: In order to solve the problem of high maneuverability and difficult trajectory prediction of hypersonic glide vehicle (HGV), an intelligent trajectory prediction method of HGV based on ensemble empirical mode decomposition and attention long short term memory network is proposed by selecting the aerodynamic acceleration as the prediction parameter. Firstly, the maneuvering characteristics and the aerodynamic variation law of HGV are analyzed based on the six degree of freedom motion equation. The dynamic tracking model is established to estimate the aerodynamic acceleration in real time. Secondly, the estimated aerodynamic acceleration is decomposed and reconstructed by using ensemble empirical mode decomposition to weaken the influence of noise and avoid interference to the prediction model. Finally, the denoised aerodynamic acceleration data used to train the attention long short term memory network. Then the future aerodynamic acceleration data predicted and the future trajectory of HGV is reconstructed to achieve online trajectory prediction. The simulation results show that the method can effectively predict the maneuver trajectory of HGV with high prediction accuracy and good stability.

Key words: Hypersonic glide vehicle, Trajectory prediction, Denoising, Ensemble empirical mode decomposition, Long short term memory network