Spatial–Temporal Physics-Constrained Multilayer Perceptron for Aircraft Trajectory Prediction

Aircraft trajectory prediction (ATP) is a critical technology for air traffic control (ATC), safeguarding aviation safety and airspace resource management. To address the limitations of existing methods—kinetic models’ susceptibility to environmental disturbances and machine learning’s lack of physical interpretability—this paper proposes a Spatial–Temporal Physics-Constrained Multilayer Perceptron (STPC-MLP) model. The model employs a spatiotemporal attention encoder to decouple timestamps and spatial coordinates (longitude, latitude, altitude), eliminating feature ambiguity caused by mixed representations. By fusing temporal and spatial attention features, it effectively extracts trajectory degradation patterns. Furthermore, a Hidden Physics-Constrained Multilayer Perceptron (HPC-MLP) integrates kinematic equations (e.g., maximum acceleration and minimum turning radius constraints) as physical regularization terms in the loss function, ensuring predictions strictly adhere to aircraft maneuvering principles. Experiments demonstrate that STPC-MLP reduces the trajectory point prediction error (RMSE) by 7.13% compared to a conventional optimal Informer model. In ablation studies, the absence of the HPC-MLP module, attention mechanism, and physical constraint loss terms significantly increased prediction errors, unequivocally validating the efficacy of the STPC-MLP architecture for trajectory prediction.