This paper systematically studies the cooperative area coverage and target tracking problem of multiple-unmanned aerial vehicles (multi-UAVs). The problem is solved by decomposing into three sub-problems: information fusion, task assignment, and multi-UAV behavior decision-making. Specifically, in the information fusion process, we use the maximum consistency protocol to update the joint estimation states of multi-targets (JESMT) and the area detection information. The area detection information is represented by the equivalent visiting time map (EVTM), which is built based on the detection probability and the actual visiting time of the area. Then, we model the task assignment problem of multi-UAV searching and tracking multi-targets as a network flow model with upper and lower flow bounds. An algorithm named task assignment minimum-cost maximum-flow (TAMM) is proposed. Cooperative behavior decision-making uses Fisher information as the mission reward to obtain the optimal tracking action of the UAV. Furthermore, a coverage behavior decision-making algorithm based on the anti-flocking method is designed for those UAVs assigned the coverage task. Finally, a distributed multi-UAV cooperative area coverage and target tracking algorithm is designed, which integrates information fusion, task assignment, and behavioral decision-making. Numerical and hardware-in-the-loop simulation results show that the proposed method can achieve persistent area coverage and cooperative target tracking.

翻译：本文系统研究了多无人机（multi-UAVs）的协同区域覆盖与目标跟踪问题。通过将问题分解为三个子问题：信息融合、任务分配和多无人机行为决策进行求解。具体而言，在信息融合过程中，我们采用最大一致性协议更新多目标联合估计状态（JESMT）和区域探测信息。区域探测信息以等效访问时间图（EVTM）表示，该图基于区域探测概率和实际访问时间构建。然后，我们将多无人机搜索与多目标跟踪的任务分配问题建模为具有上下流量界约束的网络流模型，并提出了一种名为任务分配最小费用最大流（TAMM）的算法。协同行为决策采用Fisher信息作为任务奖励，以获取无人机的最优跟踪动作。此外，针对执行覆盖任务的无人机，设计了一种基于反群聚法的覆盖行为决策算法。最终，构建了融合信息融合、任务分配与行为决策的分布式多无人机协同区域覆盖与目标跟踪算法。数值仿真与硬件在环仿真结果表明，所提方法可实现持久区域覆盖与协同目标跟踪。