Cooperative digital twins enhanced UAV topology optimization for multi-target tracking

Unmanned Aerial Vehicles-based Multiple Targets
Tracking (UAV-MTT) has been mainstream in serving missioncritical scenarios for public safety, such as hit-and-run tracking
and border patrol. Nonetheless, it is challenging to implement
high-efficiency UAV topology control due to the variable moving
speeds of targets and the limited sensing and communication
resources of UAVs. To address the problem, we propose a
terminal-edge cooperative Digital Twin (DT) framework for realtime and accurate MTT. Based on the DT technology, we achieve
joint optimization of local and global UAV topologies to track
targets with diverse speeds. Explicitly, we construct time-spatial
DT models based on temporal and spatial information of targets
and UAVs. The DT models can instruct UAVs to dynamically
adjust position relations among one-hop neighbors for local
topology optimization using our proposed Time Spatial Graph
Learning based DT (TSGL-DT) algorithm. UAVs can use the
optimization results to invite feasible neighbors to track lowspeed moving targets. Our DT models can also allocate feasible
UAVs to connect suitable local topologies for global topology
optimization. It can achieve cooperative MTT to track highspeed moving targets. The experiment results demonstrate that
our solution reduces the MTT latency by 41.2% while improving
the successful tracking ratio delivery ratio by 15.6% on average
compared to state-of-the-art benchmarks.