Attention-Enhanced Contrastive BiLSTM for UAV Intention Recognition Under Information Uncertainty
The widespread deployment of unmanned aerial vehicles (UAVs) in modern warfare has profoundly increased the complexity and dynamic nature of aerial combat. To address the limitations of traditional UAV combat intention recognition methods, which rely on the “complete information” assumption and stru...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/4/319 |
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| author | Qianru Niu Luyuan Zhang Shuangyin Ren Wei Gao Chunjiang Wang |
| author_facet | Qianru Niu Luyuan Zhang Shuangyin Ren Wei Gao Chunjiang Wang |
| author_sort | Qianru Niu |
| collection | DOAJ |
| description | The widespread deployment of unmanned aerial vehicles (UAVs) in modern warfare has profoundly increased the complexity and dynamic nature of aerial combat. To address the limitations of traditional UAV combat intention recognition methods, which rely on the “complete information” assumption and struggle to adapt effectively to dynamic adversarial environments, this paper proposes a deep learning-based UAV air combat intention recognition model (BLAC). The BLAC model establishes dynamic temporal feature mappings through a bidirectional long short-term memory network (BL) and innovatively incorporates a cross-attention mechanism (A) paired with contrastive learning (C) to improve model performance. To mitigate battlefield information uncertainty, the BLAC model implements cubic spline interpolation for numerical features and proximity-based imputation for non-numerical features, effectively resolving data loss challenges. The experimental results demonstrate that the BLAC model achieves superior intention recognition accuracy compared to mainstream models, maintaining over 91% accuracy even under 30% data loss conditions. These outcomes confirm the robustness and adaptability of the model in dynamic combat environments. This research not only provides an efficient framework for UAV combat intention recognition under information uncertainty but also offers valuable theoretical and practical insights for advancing intelligent command and control systems. |
| format | Article |
| id | doaj-art-bd0d08a67974452eb0875f95ccba213b |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-bd0d08a67974452eb0875f95ccba213b2025-08-20T02:17:20ZengMDPI AGDrones2504-446X2025-04-019431910.3390/drones9040319Attention-Enhanced Contrastive BiLSTM for UAV Intention Recognition Under Information UncertaintyQianru Niu0Luyuan Zhang1Shuangyin Ren2Wei Gao3Chunjiang Wang4Institute of Systems Engineering, Academy of Military Sciences, Beijing 100141, ChinaInstitute of Systems Engineering, Academy of Military Sciences, Beijing 100141, ChinaInstitute of Systems Engineering, Academy of Military Sciences, Beijing 100141, ChinaInstitute of Systems Engineering, Academy of Military Sciences, Beijing 100141, ChinaInstitute of Systems Engineering, Academy of Military Sciences, Beijing 100141, ChinaThe widespread deployment of unmanned aerial vehicles (UAVs) in modern warfare has profoundly increased the complexity and dynamic nature of aerial combat. To address the limitations of traditional UAV combat intention recognition methods, which rely on the “complete information” assumption and struggle to adapt effectively to dynamic adversarial environments, this paper proposes a deep learning-based UAV air combat intention recognition model (BLAC). The BLAC model establishes dynamic temporal feature mappings through a bidirectional long short-term memory network (BL) and innovatively incorporates a cross-attention mechanism (A) paired with contrastive learning (C) to improve model performance. To mitigate battlefield information uncertainty, the BLAC model implements cubic spline interpolation for numerical features and proximity-based imputation for non-numerical features, effectively resolving data loss challenges. The experimental results demonstrate that the BLAC model achieves superior intention recognition accuracy compared to mainstream models, maintaining over 91% accuracy even under 30% data loss conditions. These outcomes confirm the robustness and adaptability of the model in dynamic combat environments. This research not only provides an efficient framework for UAV combat intention recognition under information uncertainty but also offers valuable theoretical and practical insights for advancing intelligent command and control systems.https://www.mdpi.com/2504-446X/9/4/319combat intention recognitiondeep learningUAVBi-LSTMattention mechanism |
| spellingShingle | Qianru Niu Luyuan Zhang Shuangyin Ren Wei Gao Chunjiang Wang Attention-Enhanced Contrastive BiLSTM for UAV Intention Recognition Under Information Uncertainty Drones combat intention recognition deep learning UAV Bi-LSTM attention mechanism |
| title | Attention-Enhanced Contrastive BiLSTM for UAV Intention Recognition Under Information Uncertainty |
| title_full | Attention-Enhanced Contrastive BiLSTM for UAV Intention Recognition Under Information Uncertainty |
| title_fullStr | Attention-Enhanced Contrastive BiLSTM for UAV Intention Recognition Under Information Uncertainty |
| title_full_unstemmed | Attention-Enhanced Contrastive BiLSTM for UAV Intention Recognition Under Information Uncertainty |
| title_short | Attention-Enhanced Contrastive BiLSTM for UAV Intention Recognition Under Information Uncertainty |
| title_sort | attention enhanced contrastive bilstm for uav intention recognition under information uncertainty |
| topic | combat intention recognition deep learning UAV Bi-LSTM attention mechanism |
| url | https://www.mdpi.com/2504-446X/9/4/319 |
| work_keys_str_mv | AT qianruniu attentionenhancedcontrastivebilstmforuavintentionrecognitionunderinformationuncertainty AT luyuanzhang attentionenhancedcontrastivebilstmforuavintentionrecognitionunderinformationuncertainty AT shuangyinren attentionenhancedcontrastivebilstmforuavintentionrecognitionunderinformationuncertainty AT weigao attentionenhancedcontrastivebilstmforuavintentionrecognitionunderinformationuncertainty AT chunjiangwang attentionenhancedcontrastivebilstmforuavintentionrecognitionunderinformationuncertainty |