D2DTracker: A Framework for Enabling Real-Time Trajectory Prediction for Agile Drone-to-Drone Tracking via Adaptive Model Selection

Tracking a target drone using another drone is crucial in various scenarios, such as protecting critical infrastructure, securing public events, enforcing no-fly zones, and countering illegal activities. However, real-time drone-to-drone tracking poses significant challenges, mainly if the target drone exhibits agile maneuvers in 3D due to the complex dynamics of unmanned aerial vehicles and the need for accurate trajectory prediction. The quality of drone-to-drone tracking depends on the accuracy of the target's predicted trajectory(e.g., position and velocity). This paper proposes the D2DTracker framework, which can generate accurate predictions of a target drone's trajectory in real-time using onboard sensing and computations. The D2DTracker's primary concept is to fit a library of predefined simple models using the target's past behavior and recent observations. The models are then used to generate multiple trajectory predictions in real time. The best model is the one that has the least root-mean-squared error (RMSE) compared with the corresponding real-time observations. The model fitting, prediction, and selection process is repeated using real-time observations to adapt to the target's changing behavior. This enables it to maintain high tracking accuracy even in challenging scenarios. The framework is demonstrated in realistic simulations of a quadcopter using the Robot Operating System (ROS), the Gazebo simulator, and the PX4 autopilot. Simulations show that the proposed method can select the best models that can generate predictions with 0.2 RMSE compared to actual observations for circular and infinity trajectory shapes. We also provide open-source software packages of the proposed framework.