Weakly Supervised Recognition of Aerial Adversarial Maneuvers via Contrastive Learning

ZHU Longjun; YUAN Weiwei; MEN Xuefeng; TONG Wei; WU Qi

doi:10.11999/JEIT250495

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ZHU Longjun, YUAN Weiwei, MEN Xuefeng, TONG Wei, WU Qi. Weakly Supervised Recognition of Aerial Adversarial Maneuvers via Contrastive Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250495

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ZHU Longjun, YUAN Weiwei, MEN Xuefeng, TONG Wei, WU Qi. Weakly Supervised Recognition of Aerial Adversarial Maneuvers via Contrastive Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250495

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Weakly Supervised Recognition of Aerial Adversarial Maneuvers via Contrastive Learning

doi: 10.11999/JEIT250495 cstr: 32379.14.JEIT250495

ZHU Longjun^{1, 2},
YUAN Weiwei³,
MEN Xuefeng⁴,
TONG Wei⁵,
WU Qi^{6
,
,}

1.
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
2.
School of Artificial Intelligence, Shanghai Normal University Tianhua College, Shanghai 201815, China
3.
College of Computer Science and Technology, Nanjing University of Aeronautics and Astrnautics, Nanjing 211106, China
4.
Beijing FISTAR TECHNOLOGY Co., Ltd., Beijing 100048, China
5.
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
6.
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Funds: The National Natural Science Foundation of China (T2325018, 62171274), Natural Science Foundation of Jiangsu Province (BK20240641)

Received Date: 2025-06-03
Rev Recd Date: 2025-08-29

Available Online: 2025-09-08

Abstract

Abstract

Objective Accurate recognition of aerial adversarial maneuvers is essential for situational awareness and tactical decision-making in modern air warfare. Conventional supervised approaches face major challenges: obtaining labeled flight data is costly due to the intensive human effort required for collection and annotation, and these methods are limited in capturing temporal dependencies inherent in sequential flight parameters. Temporal dynamics are crucial for describing the evolution of maneuvers, yet existing models fail to fully exploit this information. To address these challenges, this study proposes a weakly supervised maneuver recognition framework based on contrastive learning. The method leverages a small proportion of labeled data to learn discriminative representations, thereby reducing reliance on extensive manual annotations. The proposed framework enhances recognition accuracy in data-scarce scenarios and provides a robust solution for maneuver analysis in dynamic adversarial aerial environments. Methods The proposed framework extends the Simple Framework for Contrastive Learning of visual Representations (SimCLR) into the time-series domain by incorporating five temporal-specific data augmentation strategies: time compression, masking, permutation, scaling, and flipping. These augmentations generate multi-view samples that form positive pairs for contrastive learning, thereby ensuring temporal invariance in the feature space. A customized ResNet-18 encoder is employed to extract hierarchical features from the augmented time-series data, and a Multi-Layer Perceptron (MLP) projection head maps these features into a contrastive space. The Normalized Temperature-scaled cross-entropy (NT-Xent) loss is adopted to maximize similarity between positive pairs and minimize it between negative pairs, which effectively mitigates pseudo-label noise. To further improve recognition performance, a fine-tuning strategy is introduced in which pre-trained features are combined with a task-specific classification head using a limited amount of labeled data to adapt to downstream recognition tasks. This contrastive learning framework enables efficient analysis of time-series flight data, achieves accurate recognition of fighter aircraft maneuvers, and reduces dependence on large-scale labeled datasets. Results and Discussions Experiments are conducted on flight simulation data obtained from DCS World. To address the class imbalance issue, hybrid datasets (Table 1) are constructed, and training data ratios ranging from 2% to 30% are employed to evaluate the effectiveness of the weakly supervised framework. The results demonstrate that contrastive learning effectively captures the temporal patterns within flight data. For example, on the D1 dataset, accuracy with the base method increases from 35.83% with 2% labeled data to 89.62% when the fine-tuning ratio reaches 30% (Tables 3–6, Fig. 2(a)–2(c)). To improve recognition of long maneuver sequences, a linear classifier and a voting strategy are introduced. The voting strategy markedly enhances few-shot learning performance. On the D1 dataset, accuracy reaches 54.5% with 2% labeled data and rises to 97.9% at a 30% fine-tuning ratio, representing a substantial improvement over the base method. On the D6 dataset, which simulates multi-source data fusion scenarios in air combat, the accuracy of the voting method increases from 0.476 with 2% labeled data to 0.928 with 30% labeled data (Fig. 2(d)–2(f)), with a growth rate in the low-data phase 53% higher than that of the base method. Additionally, on the comprehensive D7 dataset, the accuracy standard deviation of the voting method is only 0.011 (Fig. 2(g), Fig. 3), significantly lower than the 0.015 observed for the base method. The superiority of the proposed framework can be attributed to two factors: the suppression of noise through integration of multiple prediction results using the voting strategy and the extraction of robust features from unlabeled data via contrastive learning pre-training. Together, these techniques enhance generalization and stability in complex scenarios, confirming the effectiveness of the method in leveraging unlabeled data and managing multi-source information. Conclusions This study applies the SimCLR framework to maneuver recognition and proposes a weakly supervised approach based on contrastive learning. By incorporating targeted data augmentation strategies and combining self-supervised learning with fine-tuning, the method exploits the latent information in time-series data, yielding substantial improvements in recognition performance under limited labeled data conditions. Experiments on simulated air combat datasets demonstrate that the framework achieves stable recognition across different data categories, offering practical insights for feature learning and model optimization in time-series classification tasks. Future research will focus on three directions: first, integrating real flight data to evaluate the model’s generalization capability in practical scenarios; second, developing dynamically adaptive data augmentation strategies to enhance performance in complex environments; and third, combining reinforcement learning and related techniques to improve autonomous decision-making in dynamic aerial missions, thereby expanding opportunities for intelligent flight operations.
- Contrastive learning,
- Maneuver recognition,
- Time Series,
- Simple Framework for Contrastive Learning of visual Representations (SimCLR),
- Data augmentation

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References(25)

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