Electromagnetic jamming is a critical countermeasure in defense interception scenarios.This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-age...Electromagnetic jamming is a critical countermeasure in defense interception scenarios.This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-agent reinforcement learning-based cooperative jamming decision-making method(MA-CJD).The proposed approach achieves high-quality and efficient target allocation,jamming mode selection,and power control.Mathematical models for radar systems and active jamming are developed to represent a multi-jammer and multi-radar electromagnetic confrontation scenario.The cooperative jamming decision-making process is then modeled as a Markov game,where the QMix multi-agent reinforcement learning algorithm is innovatively applied to handle inter-jammer cooperation.To tackle the challenges of a parameterized action space,the MP-DQN network structure is adopted,forming the basis of the MA-CJD algorithm.Simulation experiments validate the effectiveness of the proposed MA-CJD algorithm.Results show that MA-CJD significantly reduces the time defense units are detected while minimizing jamming resource consumption.Compared with existing algorithms,MA-CJD achieves better solutions,demonstrating its superiority in cooperative jamming scenarios.展开更多
文摘Electromagnetic jamming is a critical countermeasure in defense interception scenarios.This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-agent reinforcement learning-based cooperative jamming decision-making method(MA-CJD).The proposed approach achieves high-quality and efficient target allocation,jamming mode selection,and power control.Mathematical models for radar systems and active jamming are developed to represent a multi-jammer and multi-radar electromagnetic confrontation scenario.The cooperative jamming decision-making process is then modeled as a Markov game,where the QMix multi-agent reinforcement learning algorithm is innovatively applied to handle inter-jammer cooperation.To tackle the challenges of a parameterized action space,the MP-DQN network structure is adopted,forming the basis of the MA-CJD algorithm.Simulation experiments validate the effectiveness of the proposed MA-CJD algorithm.Results show that MA-CJD significantly reduces the time defense units are detected while minimizing jamming resource consumption.Compared with existing algorithms,MA-CJD achieves better solutions,demonstrating its superiority in cooperative jamming scenarios.
