Intelligent UAV Swarm Cooperation for Multiple Targets Tracking

With the advantages of easy deployment and flexible usage, unmanned aerial vehicle (UAV) has advanced the multitarget tracking (MTT) applications. The UAV-MTT system has great potentials to execute dull, dangerous, and critical missions for frontier defense and security. A key challenge in UAV-MTT is how to coordinate multiple UAVs to track diverse invading targets accurately and consecutively. In this article, we propose a UAV swarm-based cooperative tracking architecture to systematically improve the UAV tracking performance. We design an intelligent UAV swarm-based cooperative algorithm for consecutive target tracking and physical collision avoidance. Moreover, we design an efficient cooperative algorithm to predict the trajectory of invading targets accurately. Our simulation results demonstrate that the swarm behaviors stay stable in realistic scenarios with perturbing obstacles. Compared with state-of-the-art solutions, such as the matched deep Q-network, our algorithms can increase tracking accuracy by 60%, reduce tracking delay by 23%, and achieve physical collision-avoidance during the tracking process.

Automated summary

In this article, a UAV swarm-based cooperative tracking architecture is proposed to improve the tracking performance of unmanned aerial vehicles (UAVs). The architecture includes intelligent cooperative algorithms for consecutive target tracking and physical collision avoidance, as well as an efficient cooperative algorithm for accurately predicting the trajectory of invading targets. Simulation results show that the swarm behaviors remain stable in realistic scenarios and the proposed algorithms outperform state-of-the-art solutions in terms of tracking accuracy and delay reduction.