  In this paper, the multi-target tracking (MTT) with an unmanned aerial
vehicle (UAV) swarm is investigated in the presence of jammers, where UAVs in
the swarm communicate with each other to exchange information of targets during
tracking. The communication between UAVs suffers from severe interference,
including inter-UAV interference and jamming, thus leading to a deteriorated
quality of MTT. To mitigate the interference and achieve MTT, we formulate a
interference minimization problem by jointly optimizing UAV's sub-swarm
division, trajectory, and power, subject to the constraint of MTT, collision
prevention, flying ability, and UAV energy consumption. Due to the multiple
coupling of sub-swarm division, trajectory, and power, the proposed
optimization problem is NP-hard. To solve this challenging problem, it is
decomposed into three subproblems, i.e., target association, path plan, and
power control. First, a cluster-evolutionary target association (CETA)
algorithm is proposed, which involves dividing the UAV swarm into the multiple
sub-swarms and individually matching these sub-swarms to targets. Second, a
jamming-sensitive and singular case tolerance (JSSCT)-artificial potential
field (APF) algorithm is proposed to plan trajectory for tracking the targets.
Third, we develop a jamming-aware mean field game (JA-MFG) power control
scheme, where a novel cost function is established considering the total
interference. Finally, to minimize the total interference, a dynamic
collaboration approach is designed. Simulation results validate that the
proposed dynamic collaboration approach reduces average total interference,
tracking steps, and target switching times by 28%, 33%, and 48%, respectively,
comparing to existing baselines.
