DeepMUTAT: Multiple Targets Tracking Algorithm Using Multiple UAVs

Unmanned aerial vehicle (UAV)-assisted networks are a promising technology in future wireless communication networks. Numerous applications related to UAV-assisted networks include mobile users (or targets) such as people, selfdriving vehicles, and robots. The location information of mobile devices is an essential element for realizing these applications. Furthermore, for providing services to mobile devices that move over time, real-time multi-target tracking is necessary. To ensure fair service provision to multiple users, unnecessary duplicated support or unsupported users should be avoided. For this reason, we consider multiple-target assignment constraints to fairly provide services. These motivations lead to the design of multiple UAV target assignment and tracking (MUTAT). In this paper, we propose a joint multi-UAV target assignment and tracking scheme to minimize target tracking errors while ensuring multiple target assignment constraints. Our proposed approach, named deep reinforcement learning-based multi-UAV target assignment and tracking (DeepMUTAT), consists of two distinct stages. In the first stage, to reduce computational complexity and ensure multiple target assignment constraints, we adopt a deep reinforcement learning (DRL)-based multi-target assignment for efficient multitarget tracking. In the second stage, to minimize tracking errors without requiring knowledge of the dynamics of targets and to avoid collisions with surrounding obstacles, we propose a DRL-based multi-target tracking approach. Based on extensive simulations, we demonstrate the performance of the proposed scheme compared to baseline schemes in terms of tracking error and tracking success probability. The proposed scheme achieved similar performance to the baseline scheme but had lower computational complexity.