BiGRU-MHA-KAN based flight training trajectory prediction

To improve the accuracy of trajectory prediction in flight training and enhance the reliability of prediction models, a deep hybrid neural network model named BiGRU-MHA-KAN is proposed, in which the bidirectional gated recurrent unit(BiGRU), multi-head attention(MHA) and Kolmogorov-Arnold networks(KAN) are integrated. The model strengthens the temporal feature extraction and nonlinear dynamic modeling through trajectory data preprocessing and reconstruction, combining with the bidirectional modeling, attention mechanisms and KAN networks. Simulated experiments systematically analyze the effect of the different parameter settings and historical data volumes on the model performance. The results demonstrate that, comparing with the other trajectory prediction models, the present method achieves an improvement in prediction accuracy by 4.81%-5.83%, while significantly reducing the mean squared error and root mean squared error, demonstrating the stronger temporal modeling capability and stability in flight training scenarios.