CALFUSE-KAN: A Multi-Scale Feature Fusion Network for Aircraft Trajectory Prediction

Aircraft trajectory prediction using Deep Learning models has become a significant challenge in the field of civil aviation. Traditional Convolutional Neural Network (CNN) models are difficult to capture the global features of the aircraft trajectories due to the fixed size of the receptive field of the convolution kernel, whereas attentional mechanism based Transformer, despite being able to handle long-term dependencies, has a high computational complexity and lacks local inductive bias. This paper proposes a multi-scale feature fusion architecture for aircraft trajectory prediction, called CALFUSE-KAN. This method employs CNN to extract local spatial features, multi-head self-attention to capture global spatial information, and an adaptive weighted feature fusion block for dynamic feature balancing, subsequently utilizing Long Short-Term Memory (LSTM) to effectively capture temporal dependencies. Moreover, integrating Kolmogorov-Arnold Network (KAN) to model high-dimensional nonlinear relationships further enhances prediction accuracy. Experiment demonstrates that the CALFUSE-KAN model reduces the average prediction error by 80.02%, 63.38%, 38.39%, and 33.49% compared to the LSTM, Transformer, CNN-KAN, and CNN-BiGRU models, respectively. These results indicate that the proposed hybrid model achieves high accuracy and robustness in predicting flight trajectories.