A Hybrid Trajectory Prediction Method for High Speed Targets based on WD-LSTM

In order to improve the prediction accuracy and model generalization ability, this paper proposes a wavelet decomposition (WD) and Long Short-Term Memory (LSTM) model to predict the target maneuver trajectory. Wavelet decomposition can decompose the trajectory time series into a series of high frequency and low frequency components. The long short-term memory network has a memory function, which can mine the hidden long-term dependencies between time series through learning. The combination of the two increases the accuracy of trajectory time series prediction. The proposed model is verified by simulation software, and the results show that the proposed model has a satisfactory accuracy for the prediction of air combat trajectory, which verifies the feasibility and effectiveness of the proposed model.