A Few-Shot Learning for Predicting Radar Receiver Interference Response Based on Distillation Meta-Learning
Establishing a behavioral model for radar equipment and ensuring the electromagnetic compatibility of radar systems in unmanned ship formations are essential for coordinated operations. Traditional radar receiver modeling methods based on supervised learning require complex models and large datasets...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10806651/ |
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| Summary: | Establishing a behavioral model for radar equipment and ensuring the electromagnetic compatibility of radar systems in unmanned ship formations are essential for coordinated operations. Traditional radar receiver modeling methods based on supervised learning require complex models and large datasets, which are costly and time-consuming. In the complex electromagnetic environment of unmanned ship formations, these methods often prove ineffective quickly. Therefore, this paper proposes a few-shot modeling method based on distillation meta-learning, integrating meta-learning and distillation learning. Firstly, model-agnostic meta-learning (MAML) divides the pre-training process into two stages, using interference data of various modulation types to train a general model. Then, distillation learning transfers knowledge from the complex pre-trained model to a simplified student model. This approach compresses model parameters while maintaining prediction accuracy, facilitating easier deployment in practical equipment. Results based on simulation data demonstrate that our proposed method can effectively predict the receiver’s interference response with minimal gradient steps and a small amount of training data. |
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| ISSN: | 2169-3536 |