Finding a New Balance Point: Intelligent Optimization of Multi-Target Cognitive Electronic Reconnaissance Strategy for Unmanned Aerial Vehicles
Netted radar system is used to implement detection of aerial targets to cover high-value military sites. To address the lack of an effective strategy for Unmanned Aerial Vehicles (UAVs) to perform Multi-Target Reconnaissance (MTR) against netted radar system, a reconnaissance pseudo-target is shaped...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10777084/ |
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| author | Yun Zhang Shixun You Yunbin Yan Qiaofeng Ou Xiang Zhu |
| author_facet | Yun Zhang Shixun You Yunbin Yan Qiaofeng Ou Xiang Zhu |
| author_sort | Yun Zhang |
| collection | DOAJ |
| description | Netted radar system is used to implement detection of aerial targets to cover high-value military sites. To address the lack of an effective strategy for Unmanned Aerial Vehicles (UAVs) to perform Multi-Target Reconnaissance (MTR) against netted radar system, a reconnaissance pseudo-target is shaped and used to guide the UAV to quickly approach a key reconnaissance area. By grouping radar positions, solving for multiple pseudo-targets, and integrating these pseudo-targets, we ultimately obtain an invisible pseudo-target that spans the entire radar detection range. The use of the pseudo-target reduces the dimensionality of the UAV’s observation state, thereby accelerating algorithm convergence. In addition, to solve the problem of the difficulty in determining the reconnaissance importance weights of multiple radars, a state-reward shaping equation combining the pseudo-target and real targets is designed. Finally, a deep reinforcement learning framework based on partially observable Markov processes is built, while a modular scenario pre-training method is used to improve the convergence of the policy network in the case of sparsely distributed high-quality samples. The experimental results show that the trained UAVs can successfully perform reconnaissance on up to six radar targets under physical constraints, and the completion rate for the MTR missions of six radars can reach 73 %. |
| format | Article |
| id | doaj-art-823fe40f45674a719f3fe7c439233057 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-823fe40f45674a719f3fe7c4392330572025-01-16T00:01:57ZengIEEEIEEE Access2169-35362024-01-011218325218326410.1109/ACCESS.2024.351033310777084Finding a New Balance Point: Intelligent Optimization of Multi-Target Cognitive Electronic Reconnaissance Strategy for Unmanned Aerial VehiclesYun Zhang0Shixun You1Yunbin Yan2https://orcid.org/0009-0004-3434-4765Qiaofeng Ou3Xiang Zhu4Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang, ChinaCounter-Drone Systems Laboratory, Nanchang Hangkong University, Nanchang, ChinaShijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang, ChinaCounter-Drone Systems Laboratory, Nanchang Hangkong University, Nanchang, ChinaCollege of Information Engineering, Nanchang Hangkong University, Nanchang, ChinaNetted radar system is used to implement detection of aerial targets to cover high-value military sites. To address the lack of an effective strategy for Unmanned Aerial Vehicles (UAVs) to perform Multi-Target Reconnaissance (MTR) against netted radar system, a reconnaissance pseudo-target is shaped and used to guide the UAV to quickly approach a key reconnaissance area. By grouping radar positions, solving for multiple pseudo-targets, and integrating these pseudo-targets, we ultimately obtain an invisible pseudo-target that spans the entire radar detection range. The use of the pseudo-target reduces the dimensionality of the UAV’s observation state, thereby accelerating algorithm convergence. In addition, to solve the problem of the difficulty in determining the reconnaissance importance weights of multiple radars, a state-reward shaping equation combining the pseudo-target and real targets is designed. Finally, a deep reinforcement learning framework based on partially observable Markov processes is built, while a modular scenario pre-training method is used to improve the convergence of the policy network in the case of sparsely distributed high-quality samples. The experimental results show that the trained UAVs can successfully perform reconnaissance on up to six radar targets under physical constraints, and the completion rate for the MTR missions of six radars can reach 73 %.https://ieeexplore.ieee.org/document/10777084/Cognitive electronic reconnaissancemulti-target electronic reconnaissancedeep reinforcement learningreconnaissance strategynetted radar system |
| spellingShingle | Yun Zhang Shixun You Yunbin Yan Qiaofeng Ou Xiang Zhu Finding a New Balance Point: Intelligent Optimization of Multi-Target Cognitive Electronic Reconnaissance Strategy for Unmanned Aerial Vehicles IEEE Access Cognitive electronic reconnaissance multi-target electronic reconnaissance deep reinforcement learning reconnaissance strategy netted radar system |
| title | Finding a New Balance Point: Intelligent Optimization of Multi-Target Cognitive Electronic Reconnaissance Strategy for Unmanned Aerial Vehicles |
| title_full | Finding a New Balance Point: Intelligent Optimization of Multi-Target Cognitive Electronic Reconnaissance Strategy for Unmanned Aerial Vehicles |
| title_fullStr | Finding a New Balance Point: Intelligent Optimization of Multi-Target Cognitive Electronic Reconnaissance Strategy for Unmanned Aerial Vehicles |
| title_full_unstemmed | Finding a New Balance Point: Intelligent Optimization of Multi-Target Cognitive Electronic Reconnaissance Strategy for Unmanned Aerial Vehicles |
| title_short | Finding a New Balance Point: Intelligent Optimization of Multi-Target Cognitive Electronic Reconnaissance Strategy for Unmanned Aerial Vehicles |
| title_sort | finding a new balance point intelligent optimization of multi target cognitive electronic reconnaissance strategy for unmanned aerial vehicles |
| topic | Cognitive electronic reconnaissance multi-target electronic reconnaissance deep reinforcement learning reconnaissance strategy netted radar system |
| url | https://ieeexplore.ieee.org/document/10777084/ |
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