Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation
In modern electronic warfare, the intelligence of the jammer greatly worsens the anti-jamming performance of traditional passive suppression methods. How to actively design anti-jamming strategies to deal with intelligent jammers is crucial to the radar system. In the existing research on radar anti...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/3/581 |
| _version_ | 1797623429538512896 |
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| author | Jie Geng Bo Jiu Kang Li Yu Zhao Hongwei Liu Hailin Li |
| author_facet | Jie Geng Bo Jiu Kang Li Yu Zhao Hongwei Liu Hailin Li |
| author_sort | Jie Geng |
| collection | DOAJ |
| description | In modern electronic warfare, the intelligence of the jammer greatly worsens the anti-jamming performance of traditional passive suppression methods. How to actively design anti-jamming strategies to deal with intelligent jammers is crucial to the radar system. In the existing research on radar anti-jamming strategies’ design, the assumption of jammers is too ideal. To establish a model that is closer to real electronic warfare, this paper explores the intelligent game between a subpulse-level frequency-agile (FA) radar and a transmit/receive time-sharing jammer under jamming power dynamic allocation. Firstly, the discrete allocation model of jamming power is established, and the multiple-round sequential interaction between the radar and the jammer is described based on an extensive-form game. A detection probability calculation method based on the signal-to-interference-pulse-noise ratio (SINR) accumulation gain criterion (SAGC) is proposed to evaluate the game results. Secondly, considering that the competition between the radar and the jammer has the feature of imperfect information, we utilized neural fictitious self-play (NFSP), an end-to-end deep reinforcement learning (DRL) algorithm, to find the Nash equilibrium (NE) of the game. Finally, the simulation results showed that the game between the radar and the jammer can converge to an approximate NE under the established model. The approximate NE strategies are better than the elementary strategies from the perspective of detection probability. In addition, comparing NFSP and the deep Q-network (DQN) illustrates the effectiveness of NFSP in solving the NE of imperfect information games. |
| first_indexed | 2024-03-11T09:28:52Z |
| format | Article |
| id | doaj.art-d8fe9109fd6a40dc807e8c9442c5b7fe |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T09:28:52Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d8fe9109fd6a40dc807e8c9442c5b7fe2023-11-16T17:51:15ZengMDPI AGRemote Sensing2072-42922023-01-0115358110.3390/rs15030581Radar and Jammer Intelligent Game under Jamming Power Dynamic AllocationJie Geng0Bo Jiu1Kang Li2Yu Zhao3Hongwei Liu4Hailin Li5National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaBeijing Institute of Tracking and Telecommunication Technology, Beijing 100094, ChinaIn modern electronic warfare, the intelligence of the jammer greatly worsens the anti-jamming performance of traditional passive suppression methods. How to actively design anti-jamming strategies to deal with intelligent jammers is crucial to the radar system. In the existing research on radar anti-jamming strategies’ design, the assumption of jammers is too ideal. To establish a model that is closer to real electronic warfare, this paper explores the intelligent game between a subpulse-level frequency-agile (FA) radar and a transmit/receive time-sharing jammer under jamming power dynamic allocation. Firstly, the discrete allocation model of jamming power is established, and the multiple-round sequential interaction between the radar and the jammer is described based on an extensive-form game. A detection probability calculation method based on the signal-to-interference-pulse-noise ratio (SINR) accumulation gain criterion (SAGC) is proposed to evaluate the game results. Secondly, considering that the competition between the radar and the jammer has the feature of imperfect information, we utilized neural fictitious self-play (NFSP), an end-to-end deep reinforcement learning (DRL) algorithm, to find the Nash equilibrium (NE) of the game. Finally, the simulation results showed that the game between the radar and the jammer can converge to an approximate NE under the established model. The approximate NE strategies are better than the elementary strategies from the perspective of detection probability. In addition, comparing NFSP and the deep Q-network (DQN) illustrates the effectiveness of NFSP in solving the NE of imperfect information games.https://www.mdpi.com/2072-4292/15/3/581electronic warfareintelligent gamejamming power dynamic allocationneural fictitious self-playdeep reinforcement learningNash equilibrium |
| spellingShingle | Jie Geng Bo Jiu Kang Li Yu Zhao Hongwei Liu Hailin Li Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation Remote Sensing electronic warfare intelligent game jamming power dynamic allocation neural fictitious self-play deep reinforcement learning Nash equilibrium |
| title | Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation |
| title_full | Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation |
| title_fullStr | Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation |
| title_full_unstemmed | Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation |
| title_short | Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation |
| title_sort | radar and jammer intelligent game under jamming power dynamic allocation |
| topic | electronic warfare intelligent game jamming power dynamic allocation neural fictitious self-play deep reinforcement learning Nash equilibrium |
| url | https://www.mdpi.com/2072-4292/15/3/581 |
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