  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.
