Collaborative Target-Tracking Algorithm Based on Trajectory Decision for UAV Swarm

Target-tracking technology has extensive applications in various fields, including reconnaissance, disaster monitoring, patrol, and environmental protection. Unmanned aerial vehicles (UAVs) equipped with target detection devices, such as cameras, and target-tracking algorithms can execute target-tracking tasks. However, developing an effective target-tracking algorithm poses significant challenges due to the limited field-of-view of cameras, kinematic constraints of UAV swarms, and the unknown motion of targets. This paper constructs a collaborative target-tracking algorithm architecture. It proposes a method for calculating target motion parameters based on multi-time-scales and multi-models. Additionally, an integrated tracking trajectory de-signed for ground maneuvering targets is developed according to the tracking patterns of UAV swarms and a fuzzy decision-making algorithm. The flight control law of UAVs is designed using the active disturbance rejection control method. Moving-target tracking flight tests are conducted, during which the tracking distance between UAVs and the target is maintained within the required range of the camera’s field of view. Moreover, UAVs maintain their geometric formation shape throughout the tracking process. The flight test results demonstrate that the UAV formation target-tracking algorithm exhibits strong robustness and can adapt to different collaborative tracking task scenarios.