Distributed auction-based adaptive task assignment and re-assignment for multi-UAV suppressive jamming

Task assignment is critical for multiple unmanned aerial vehicles (UAVs) to perform suppressive jamming against radar network. However, under abrupt state changes of enemy radars, centralized methods often incur high computational complexity and exhibit limited robustness, rendering them unsuitable for rapid task re-assignment. To address these challenges, an adaptive multi-UAV task assignment and re-assignment scheme for suppressive jamming against intermittent radar network is proposed. Specifically, the system is comprehensively modeled by integrating a motion model describing high-value target trajectory, a reconnaissance model detecting radar state transitions, and a suppressive jamming model characterizing the matching relationships between UAVs and radars. The problem is formulated as a dynamic integer program with time-varying constraints. To solve this, a distributed auction-based task assignment and re-assignment algorithm is proposed, enabling task assignment and re-assignment triggered by sudden radar activations or deactivations. Simulation results demonstrate that the proposed approach achieves jamming performance comparable to centralized methods, outperforms traditional fixed and random strategies, and enables re-assignment in response to abrupt radar state changes.