Deep learning-based flight path prediction for optical UAV tracking
Christopher Naverschnigg, Denis Ojdanić, Andreas Sinn, Georg Schitter · 2025
This paper presents a deep learning-based approach for the application of flight path prediction to enhance the robustness of optical UAV detection systems, which combine camera systems and a pan/tilt mount. A Temporal Fusion Transformer algorithm for time series forecasting is trained using a synthetic data set of UAV flight trajectories to enable flight path prediction. The success rate, defined as the percentage of total tracks where the UAV remains within the field of view after a certain prediction horizon, is evaluated using a test dataset consisting of footage captured through a telescope-based optical UAV detection system during multiple field tests. The results are then compared to those obtained using a conventional Kalman filter-based approach. For prediction horizons up to 1s both algorithms achieve a success rate above 0.75. For an increasing prediction horizon up to 10s the trained Temporal Fusion Transformer outperforms the Kalman Filter by a factor of up to 1.3.