Unmanned aerial vehicle (UAV) is widely used for military, delivery, and disaster because of many advantages of network formation, high mobility, and low cost. It is expected that the UAV control technique for target tracking is essential because UAV collects environmental knowledge and provides an effective wireless network to ground users. We have been studying deep reinforcement learning (DRL) based on multiple UAV control for target (i.e., ground user) tracking. DRL is an emerging tool to select optimal action in a dynamic system, for example, autonomous driving, UAV control problem and wireless network, etc. By applying DRL to multiple UAVs system, UAVs learn how to take actions to track/localize the ground target in a complex 3D environment. Besides, the DRL-based UAV control technique mitigates computation time that becomes critical when the number of UAVs or targets increases.
Fig 1. Multiple UAVs track ground users in challenging 3D environment
Multiple UAVs are assigned to tracking missions in the existence of multiple ground targets. Each of the UAVs is equipped with a range sensor that measures the distance between UAVs and targets. The measurements received by UAVs are blocked due to obstacles and structure.
Fig 2. The results of 3D trajectories of UAV and target (left), and localization error (right) during the tracking missions
While operating tracking missions, UAV’s flight direction is determined by the trained-DRL network. Tracking error measures MSE between the true target position and estimated target position. Applying DRL to target tracking enables UAVs to track and localize ground users accurately.