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

Integrated Design of Cooperative Area Coverage and Target Tracking with Multi-UAV System