Visual-Based Moving Target Tracking With Solar-Powered Fixed-Wing UAV: A New Learning-Based Approach

The use of legitimate unmanned aerial vehicles (UAVs) to surveil and track misbehaved UAVs can serve a crucial role in public safety and security. This paper proposes a new deep reinforcement learning (DRL)-based online control scheme for visual-based UAV-on-UAV tracking and monitoring, where a solar-powered, fixed-wing UAV tracks a suspicious UAV target by having the target inside its effective visual range. The key idea is a new deep deterministic policy gradient (DDPG)-based model, which can cope with the continuous state and action spaces of the monitor and learn the optimal acceleration control policy adapting to the solar power availability and the target's movement. The state space is designed to be the relative position of the monitor to the target, thereby preventing model infeasibility. Experiments show that the new algorithm can maintain a desired distance from the target, and outperform control-and optimization-based alternatives in terms of energy efficiency and tracking accuracy. An interesting finding is that our algorithm learns faster and better with a constraint of a minimum allowed battery energy reserve. The reason is that, without the constraint, the monitor is more likely to deplete its battery before the end of a surveillance mission.