Graph Learning Enhanced UAV Swarms Based Multiple Targets Tracking

Abstract

With the development of Artificial Intelligence (AI) technology, diverse Internet of Things (IoT) devices digesting abundant data have been exploited to meet more application requirements. In this regard, Unmanned Aerial Vehicle-based Multiple Targets Tracking (UAV-MTT) applications have been paid attention to processing a vast amount of sensing information for accurate and consecutive MTT. However, this application exposes imperative computing requirements on resource-limited UAVs. Edge computing can provide extra resources to alleviate the computing pressure for high-efficiency tracking decisions. Nonetheless, it is challenging to dynamically allocate UAVs for optimal association with time-varying target trajectories. To address the mentioned problems, we propose a terminal-edge cooperative tracking framework with a cross-layer resource cooperation method. In this design, we propose an auction-based cooperative game algorithm to implement highly accurate trajectory prediction. We then propose a graph learning-based tracking algorithm to adaptively manage the dynamic UAV topology for consecutive MTT. Simulation results demonstrate that our algorithm improves 70% prediction accuracy compared to other benchmarks while saving 40% energy consumption.