Distributed Task Assignment for Multi-UAV Suppressive Jamming Under Communication Failures

In modern electronic warfare, task assignment is a critical challenge in enabling multiple unmanned aerial vehicles (UAVs) to perform effective suppressive jamming. However, under communication disruptions or UAVs failure, centralized task assignment approaches underperform due to high communication overhead, low robustness, and slow responsiveness. To address these issues, a distributed suppressive jamming task assignment method for multiple UAVs is investigated under communication failures. A system model is established, which includes a highvalue target motion model to indicate various target movement patterns, a suppressive jamming model to represent the matching relationship between UAVs and radars, and a communication model to describe communication link failures. Based on this, the problem is formulated as a distributed discrete optimization problem with communication constraints. Accordingly, a distributed dynamic task assignment algorithm is proposed, which rapidly solves task assignments in static environments, reuses previous solutions to save computational resources in dynamic environments, and handles task re-assignment in the event of UAV failures. Simulation results show that the proposed algorithm exhibits rapid convergence in static and dynamic environments, robust performance under varying communication failure transfer probabilities and relay UAV numbers, scalability with increasing UAV quantities, and jamming performance comparable to centralized methods, outperforming traditional fixed assignment approaches.