Track Deception by UAV Swarm Against Radar Network with Limited Communication

In this paper, a track deception scheme by unmanned aerial vehicle (UAV) swarm against radar network with limited communication is proposed. Firstly, a track deception model for single UAV is established, along with a communication framework for information exchange within the swarm. To address packet dropouts, a track deception scheme based on predictive mechanism is introduced, in which cluster head (CH) UAVs estimate the state information of cluster member (CM) UAVs and generate optimal control decisions over multiple time steps. Based on this mechanism, a trajectory planning optimization model is developed with the objective of minimizing the flight velocity of UAVs, subject to kinematic performance constraints and safe distance requirements. This optimization problem is solved by particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the phantom tracks generated with limited communication can effectively pass the homology test from radar network, validating the robustness and feasibility of the proposed scheme.