Physics-guided residual learning for phase-aware UAV trajectory prediction in urban environments

Abstract

Reliable and accurate trajectory prediction is critical for the safe and efficient operation of unmanned aerial vehicles (UAVs) in complex urban environments, where flight dynamics are subject to wind disturbances, dense obstacle fields, and strongly phase-dependent behavior. Conventional physics-based approaches are limited by parameter uncertainty, simplifying assumptions, and unmodeled disturbances, while data-driven models may lack physical plausibility and robustness across different flight phases. To address these limitations, this paper proposes a physics-guided hybrid residual-correction framework for UAV trajectory prediction. The approach combines a physics-based model with a data-driven sequence model and learns a residual correction that compensates for the deviation between analytical prediction and observed flight behavior. In addition, the model is trained with physics-guided feasibility regularization to promote realistic speed, acceleration, jerk, and landing descent behavior. Experimental evaluation on a real-world test set shows that the proposed method yields improved results relative to the stand-alone physics model, the LSTM model, and an MLP-based fusion model across all major metrics, including RMSE, MAE, ADE, and FDE. Phase-wise analysis further demonstrates strong improvements in cruise and landing, while highlighting takeoff as the most challenging phase. The results indicate that combining physical structure with learned residual correction provides a more accurate, physically consistent, and operationally interpretable approach for UAV trajectory forecasting.