Adaptive Missile Avoidance Algorithm for UAV Based on Multi-Head Attention Mechanism and Dual Population Confrontation Game
In recent years, UAVs have faced increasingly severe and diversified missile threats. To address the challenge that reinforcement learning-based missile evasion algorithms struggle to adapt to various unknown missile types, we introduce a risk-sensitive PPO algorithm and propose a training framework...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/5/382 |
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| author | Cheng Zhang Junhao Song Chengyang Tao Zitao Su Zhiqiang Xu Weijia Feng Zhaoxiang Zhang Yuelei Xu |
| author_facet | Cheng Zhang Junhao Song Chengyang Tao Zitao Su Zhiqiang Xu Weijia Feng Zhaoxiang Zhang Yuelei Xu |
| author_sort | Cheng Zhang |
| collection | DOAJ |
| description | In recent years, UAVs have faced increasingly severe and diversified missile threats. To address the challenge that reinforcement learning-based missile evasion algorithms struggle to adapt to various unknown missile types, we introduce a risk-sensitive PPO algorithm and propose a training framework incorporating multi-head attention mechanisms and dual-population adversarial training. The multi-head attention mechanism enables the policy network to extract latent features such as missile guidance laws from state sequences, while the dual-population adversarial approach ensures policy diversity and robustness. Compared to conventional self-play methods and GRU-based evasion strategies, our method demonstrates superior training efficiency and generates evasion policies with better adaptability to different missile types. |
| format | Article |
| id | doaj-art-8fdd7f00794046809b64d5eae7865b29 |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-8fdd7f00794046809b64d5eae7865b292025-08-20T03:14:39ZengMDPI AGDrones2504-446X2025-05-019538210.3390/drones9050382Adaptive Missile Avoidance Algorithm for UAV Based on Multi-Head Attention Mechanism and Dual Population Confrontation GameCheng Zhang0Junhao Song1Chengyang Tao2Zitao Su3Zhiqiang Xu4Weijia Feng5Zhaoxiang Zhang6Yuelei Xu7Unmanned System Intelligent Perception and Collaboration Technology Laboratory, Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710129, ChinaUnmanned System Intelligent Perception and Collaboration Technology Laboratory, Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710129, ChinaUnmanned System Intelligent Perception and Collaboration Technology Laboratory, Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710129, ChinaUnmanned System Intelligent Perception and Collaboration Technology Laboratory, Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710129, ChinaUnmanned System Intelligent Perception and Collaboration Technology Laboratory, Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710129, ChinaUnmanned System Intelligent Perception and Collaboration Technology Laboratory, Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710129, ChinaUnmanned System Intelligent Perception and Collaboration Technology Laboratory, Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710129, ChinaUnmanned System Intelligent Perception and Collaboration Technology Laboratory, Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710129, ChinaIn recent years, UAVs have faced increasingly severe and diversified missile threats. To address the challenge that reinforcement learning-based missile evasion algorithms struggle to adapt to various unknown missile types, we introduce a risk-sensitive PPO algorithm and propose a training framework incorporating multi-head attention mechanisms and dual-population adversarial training. The multi-head attention mechanism enables the policy network to extract latent features such as missile guidance laws from state sequences, while the dual-population adversarial approach ensures policy diversity and robustness. Compared to conventional self-play methods and GRU-based evasion strategies, our method demonstrates superior training efficiency and generates evasion policies with better adaptability to different missile types.https://www.mdpi.com/2504-446X/9/5/382missile evasionreinforcement learning intelligent control of UAVsmulti-head attention mechanismdual population confrontation game |
| spellingShingle | Cheng Zhang Junhao Song Chengyang Tao Zitao Su Zhiqiang Xu Weijia Feng Zhaoxiang Zhang Yuelei Xu Adaptive Missile Avoidance Algorithm for UAV Based on Multi-Head Attention Mechanism and Dual Population Confrontation Game Drones missile evasion reinforcement learning intelligent control of UAVs multi-head attention mechanism dual population confrontation game |
| title | Adaptive Missile Avoidance Algorithm for UAV Based on Multi-Head Attention Mechanism and Dual Population Confrontation Game |
| title_full | Adaptive Missile Avoidance Algorithm for UAV Based on Multi-Head Attention Mechanism and Dual Population Confrontation Game |
| title_fullStr | Adaptive Missile Avoidance Algorithm for UAV Based on Multi-Head Attention Mechanism and Dual Population Confrontation Game |
| title_full_unstemmed | Adaptive Missile Avoidance Algorithm for UAV Based on Multi-Head Attention Mechanism and Dual Population Confrontation Game |
| title_short | Adaptive Missile Avoidance Algorithm for UAV Based on Multi-Head Attention Mechanism and Dual Population Confrontation Game |
| title_sort | adaptive missile avoidance algorithm for uav based on multi head attention mechanism and dual population confrontation game |
| topic | missile evasion reinforcement learning intelligent control of UAVs multi-head attention mechanism dual population confrontation game |
| url | https://www.mdpi.com/2504-446X/9/5/382 |
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