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JIn this paper, we introduce an approach to real- time 3D Unmanned Aerial Vehicle (UAV) trajectory prediction using Gated Recurrent Neural Networks (GRUs). Our study focuses on a UAV monitored by an observer, such as an inter- ceptor drone, offering pivotal insights for aerial surveillance and defense. We explore and compare 24 GRU models to predict 3D positions and velocities, establishing that models predicting positions from velocity data and known sampling times excel in accuracy and generalizability over direct position prediction models. A significant contribution of our work is the creation of a comprehensive dataset of synthetic 3D UAV trajectories, leveraging the Gazebo robotics simulator and PX4 autopilot for realism. This dataset is enriched by varying agility characteristics, including different curvatures and speeds, and augmented with open-source datasets like FPV UZH and Mid-Air. We carefully analyze GRU models trained on this integrated dataset, assessing the influence of dataset diversity on model performance. Further, we validate our models in a real- time, realistic simulated environment using Gazebo and PX4, demonstrating their efficacy in real-time data prediction. Our experimental results show that GRU models with integrated Max normalized dataset achieved relative performance with average MSE ranging from 2×10−8 to 2×10−7. Further, results suggest that velocity based trajectories are more generalized and robust than position prediction across different models for real time performance.

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