Multi-UAV Resource Allocation and Trajectory Planning via Hierarchical Coalition Game for Radar Network Suppression

This paper investigates the problem of resource allocation and trajectory planning for multi-unmanned aerial vehicle (UAV) suppressive jamming. A system model is established, which includes a motion model to describe the trajectories of UAVs and the high-value target (HVT), a resource allocation model to represent beam and power allocation of UAVs, and a radar detection model to quantify the detection probability under suppressive jamming. A utility function is designed as a weighted sum of multiple reward and cost terms, and the optimization problem is formulated as a mixed-integer programming (MIP) model that maximizes the utility subject to motion dynamics and resource allocation constraints. To solve this problem, a hierarchical coalition formation game–particle swarm optimization (CFG-PSO) algorithm is proposed, which alternately iterates between CFG-based jamming beam allocation and PSO-based power allocation and trajectory planning. Furthermore, potential game theory is employed to prove that the constructed CFG converges to a Nash-stable coalition partition. Simulation results demonstrate that the proposed algorithm exhibits adaptability to various threat-region configurations, UAV initial positions, and HVT maneuvers, and outperforms traditional fixed allocation and trajectory strategies.