A Few-shot Learning Method for Intent Analysis of Air Combat Confrontation Behaviors
Aiming at the problems of multiple data sources, multiple data modes, high data dimensions, large redundancy, small and unbalanced sample size, and difficulty in obtaining a large number of labeled data required for training, a deep bidirectional gated recurrent unit (DBGRU) electromagnetic behavior...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2024-08-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2348 |
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| _version_ | 1849702350252408832 |
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| author | PAN Ming ZHENG Jingsong LI Jinliang FANG Long YANG Yang ZHAO Shijie |
| author_facet | PAN Ming ZHENG Jingsong LI Jinliang FANG Long YANG Yang ZHAO Shijie |
| author_sort | PAN Ming |
| collection | DOAJ |
| description | Aiming at the problems of multiple data sources, multiple data modes, high data dimensions, large redundancy, small and unbalanced sample size, and difficulty in obtaining a large number of labeled data required for training, a deep bidirectional gated recurrent unit (DBGRU) electromagnetic behavior intent recognition model is constructed. By integrating the attention mechanism in the Bidirectional Gated Recurrent Unit (BiGRU) , the feature learning ability of the model is improved, and adaptively assign the weight of different air combat feature information. With DBGRU as the backbone network, a few-shot contrastive learning algorithm based on data augmentation is proposed, which uses the Wasserstein Generative Adversarial Network (WGAN) based on Wasserstein distance to enrich the original data, and uses the contrastive learning framework to mine the rich pattern information in the multimodal data to make up for the lack of few-shot data, so as to accurately predict the behavior intention of electromagnetic targets. The experimental simulation results show that the accuracy of the few-shot contrastive learning algorithm based on data augmentation in predicting the behavior intention of few-shot air combat targets is 91. 13% . |
| format | Article |
| id | doaj-art-15b6ee1ebc184efdbc0ab3ccd244f49f |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-15b6ee1ebc184efdbc0ab3ccd244f49f2025-08-20T03:17:40ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832024-08-012904505810.15938/j.jhust.2024.04.006A Few-shot Learning Method for Intent Analysis of Air Combat Confrontation BehaviorsPAN Ming0ZHENG Jingsong1LI Jinliang2FANG Long3YANG Yang4ZHAO Shijie5Southwest China Research Institute of Electronic Equipment, Chengdu 610036 , ChinaSouthwest China Research Institute of Electronic Equipment, Chengdu 610036 , ChinaSouthwest China Research Institute of Electronic Equipment, Chengdu 610036 , ChinaSchool of Automation, Northwestern Polytechnic University, Xi ′an 710129 , ChinaSchool of Automation, Northwestern Polytechnic University, Xi ′an 710129 , ChinaSchool of Automation, Northwestern Polytechnic University, Xi ′an 710129 , ChinaAiming at the problems of multiple data sources, multiple data modes, high data dimensions, large redundancy, small and unbalanced sample size, and difficulty in obtaining a large number of labeled data required for training, a deep bidirectional gated recurrent unit (DBGRU) electromagnetic behavior intent recognition model is constructed. By integrating the attention mechanism in the Bidirectional Gated Recurrent Unit (BiGRU) , the feature learning ability of the model is improved, and adaptively assign the weight of different air combat feature information. With DBGRU as the backbone network, a few-shot contrastive learning algorithm based on data augmentation is proposed, which uses the Wasserstein Generative Adversarial Network (WGAN) based on Wasserstein distance to enrich the original data, and uses the contrastive learning framework to mine the rich pattern information in the multimodal data to make up for the lack of few-shot data, so as to accurately predict the behavior intention of electromagnetic targets. The experimental simulation results show that the accuracy of the few-shot contrastive learning algorithm based on data augmentation in predicting the behavior intention of few-shot air combat targets is 91. 13% .https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2348intent recognitionattention mechanismsgated recurrent unitgenerative adversarial networkscontrastive learning |
| spellingShingle | PAN Ming ZHENG Jingsong LI Jinliang FANG Long YANG Yang ZHAO Shijie A Few-shot Learning Method for Intent Analysis of Air Combat Confrontation Behaviors Journal of Harbin University of Science and Technology intent recognition attention mechanisms gated recurrent unit generative adversarial networks contrastive learning |
| title | A Few-shot Learning Method for Intent Analysis of Air Combat Confrontation Behaviors |
| title_full | A Few-shot Learning Method for Intent Analysis of Air Combat Confrontation Behaviors |
| title_fullStr | A Few-shot Learning Method for Intent Analysis of Air Combat Confrontation Behaviors |
| title_full_unstemmed | A Few-shot Learning Method for Intent Analysis of Air Combat Confrontation Behaviors |
| title_short | A Few-shot Learning Method for Intent Analysis of Air Combat Confrontation Behaviors |
| title_sort | few shot learning method for intent analysis of air combat confrontation behaviors |
| topic | intent recognition attention mechanisms gated recurrent unit generative adversarial networks contrastive learning |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2348 |
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