Situational continuity-based air combat autonomous maneuvering decision-making
In order to improve the performance of UAV’s autonomous maneuvering decision-making, this paper proposes a decision-making method based on situational continuity. The algorithm in this paper designs a situation evaluation function with strong guidance, then trains the Long Short-Term Memory (LSTM) u...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-11-01
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| Series: | Defence Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914722001842 |
| _version_ | 1797627108361502720 |
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| author | Jian-dong Zhang Yi-fei Yu Li-hui Zheng Qi-ming Yang Guo-qing Shi Yong Wu |
| author_facet | Jian-dong Zhang Yi-fei Yu Li-hui Zheng Qi-ming Yang Guo-qing Shi Yong Wu |
| author_sort | Jian-dong Zhang |
| collection | DOAJ |
| description | In order to improve the performance of UAV’s autonomous maneuvering decision-making, this paper proposes a decision-making method based on situational continuity. The algorithm in this paper designs a situation evaluation function with strong guidance, then trains the Long Short-Term Memory (LSTM) under the framework of Deep Q Network (DQN) for air combat maneuvering decision-making. Considering the continuity between adjacent situations, the method takes multiple consecutive situations as one input of the neural network. To reflect the difference between adjacent situations, the method takes the difference of situation evaluation value as the reward of reinforcement learning. In different scenarios, the algorithm proposed in this paper is compared with the algorithm based on the Fully Neural Network (FNN) and the algorithm based on statistical principles respectively. The results show that, compared with the FNN algorithm, the algorithm proposed in this paper is more accurate and forward-looking. Compared with the algorithm based on the statistical principles, the decision-making of the algorithm proposed in this paper is more efficient and its real-time performance is better. |
| first_indexed | 2024-03-11T10:19:42Z |
| format | Article |
| id | doaj.art-734d8fac635940a4ab85a760b0e038cd |
| institution | Directory Open Access Journal |
| issn | 2214-9147 |
| language | English |
| last_indexed | 2024-03-11T10:19:42Z |
| publishDate | 2023-11-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj.art-734d8fac635940a4ab85a760b0e038cd2023-11-16T06:10:01ZengKeAi Communications Co., Ltd.Defence Technology2214-91472023-11-01296679Situational continuity-based air combat autonomous maneuvering decision-makingJian-dong Zhang0Yi-fei Yu1Li-hui Zheng2Qi-ming Yang3Guo-qing Shi4Yong Wu5School of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, China; Military Representative Office of Haizhuang Wuhan Bureau in Luoyang Region, 471000, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, China; Corresponding author.School of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, ChinaIn order to improve the performance of UAV’s autonomous maneuvering decision-making, this paper proposes a decision-making method based on situational continuity. The algorithm in this paper designs a situation evaluation function with strong guidance, then trains the Long Short-Term Memory (LSTM) under the framework of Deep Q Network (DQN) for air combat maneuvering decision-making. Considering the continuity between adjacent situations, the method takes multiple consecutive situations as one input of the neural network. To reflect the difference between adjacent situations, the method takes the difference of situation evaluation value as the reward of reinforcement learning. In different scenarios, the algorithm proposed in this paper is compared with the algorithm based on the Fully Neural Network (FNN) and the algorithm based on statistical principles respectively. The results show that, compared with the FNN algorithm, the algorithm proposed in this paper is more accurate and forward-looking. Compared with the algorithm based on the statistical principles, the decision-making of the algorithm proposed in this paper is more efficient and its real-time performance is better.http://www.sciencedirect.com/science/article/pii/S2214914722001842UAVManeuvering decision-makingSituational continuityLong short-term memory (LSTM)Deep Q network (DQN)Fully neural network (FNN) |
| spellingShingle | Jian-dong Zhang Yi-fei Yu Li-hui Zheng Qi-ming Yang Guo-qing Shi Yong Wu Situational continuity-based air combat autonomous maneuvering decision-making Defence Technology UAV Maneuvering decision-making Situational continuity Long short-term memory (LSTM) Deep Q network (DQN) Fully neural network (FNN) |
| title | Situational continuity-based air combat autonomous maneuvering decision-making |
| title_full | Situational continuity-based air combat autonomous maneuvering decision-making |
| title_fullStr | Situational continuity-based air combat autonomous maneuvering decision-making |
| title_full_unstemmed | Situational continuity-based air combat autonomous maneuvering decision-making |
| title_short | Situational continuity-based air combat autonomous maneuvering decision-making |
| title_sort | situational continuity based air combat autonomous maneuvering decision making |
| topic | UAV Maneuvering decision-making Situational continuity Long short-term memory (LSTM) Deep Q network (DQN) Fully neural network (FNN) |
| url | http://www.sciencedirect.com/science/article/pii/S2214914722001842 |
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