A cooperative jamming decision-making method based on multi-agent reinforcement learning
Abstract 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 metho...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-02-01
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| Series: | Autonomous Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s43684-025-00090-4 |
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| _version_ | 1823861763451387904 |
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| author | Bingchen Cai Haoran Li Naimin Zhang Mingyu Cao Han Yu |
| author_facet | Bingchen Cai Haoran Li Naimin Zhang Mingyu Cao Han Yu |
| author_sort | Bingchen Cai |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-db66135ee8d34eae9450d5ca84c815c2 |
| institution | Kabale University |
| issn | 2730-616X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Autonomous Intelligent Systems |
| spelling | doaj-art-db66135ee8d34eae9450d5ca84c815c22025-02-09T12:47:28ZengSpringerAutonomous Intelligent Systems2730-616X2025-02-015111510.1007/s43684-025-00090-4A cooperative jamming decision-making method based on multi-agent reinforcement learningBingchen Cai0Haoran Li1Naimin Zhang2Mingyu Cao3Han Yu4Beijing Institute of Astronautical Systems EngineeringBeijing Institute of Astronautical Systems EngineeringBeijing Institute of Astronautical Systems EngineeringBeijing Institute of Astronautical Systems EngineeringBeijing Institute of Astronautical Systems EngineeringAbstract 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.https://doi.org/10.1007/s43684-025-00090-4Electromagnetic jammingMulti-agent systemReinforcement learningCooperative decision-making |
| spellingShingle | Bingchen Cai Haoran Li Naimin Zhang Mingyu Cao Han Yu A cooperative jamming decision-making method based on multi-agent reinforcement learning Autonomous Intelligent Systems Electromagnetic jamming Multi-agent system Reinforcement learning Cooperative decision-making |
| title | A cooperative jamming decision-making method based on multi-agent reinforcement learning |
| title_full | A cooperative jamming decision-making method based on multi-agent reinforcement learning |
| title_fullStr | A cooperative jamming decision-making method based on multi-agent reinforcement learning |
| title_full_unstemmed | A cooperative jamming decision-making method based on multi-agent reinforcement learning |
| title_short | A cooperative jamming decision-making method based on multi-agent reinforcement learning |
| title_sort | cooperative jamming decision making method based on multi agent reinforcement learning |
| topic | Electromagnetic jamming Multi-agent system Reinforcement learning Cooperative decision-making |
| url | https://doi.org/10.1007/s43684-025-00090-4 |
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