Joint Work Mode Selection and Resource Allocation for Radar Networks Against Mainlobe Jamming
Mainlobe jamming is an essential threat to radars.Resource management collaborating multiple radars is an effective strategy to counter such threats.In this paper,a reinforcement learning-based technique of dynamically selecting multiple radar ac-tive and passive work modes is proposed to improve the overall performance of multi-target tracking under mainlobe jamming.The active measurement and passive localization models in jamming conditions are presented.An objective function to minimize tracking error is designed by utilizing the opposite correlation between precision and jamming intensity of the two work modes.Finally,a re-inforcement learning agent employing the proximal policy optimization algorithm is used to select the radars'work modes.The re-maining variables of multi-radar dwell time are then optimized as a convex optimization problem.Simulation results demonstrate that,compared to per-determined work mode selection strategies,the proposed approach improves the tracking performance of the radar network in jamming environments,thereby improving the anti-jamming capability.
万方数据
维普
