用信号分解与注意力机制，仅用10个符号实现96%的发射源识别准确率。
Few-Shot Specific Emitter Identification via Integrated Complex Variational Mode Decomposition and Spatial Attention Transfer
- 通过复数变分模态分解重构信号，提升特征提取精度。
- 结合时序卷积与空间注意力，使模型在10符号下达96%准确率。
- 无需先验知识，适合小样本真实场景，可复用预训练权重。
特定发射源识别（SEI）利用被动硬件特征认证发射器，是可靠的物理层安全方案。然而，多数基于深度学习的方法依赖大量数据或先验信息，在真实场景中小样本条件下难以应用。本文提出一种集成复数变分模态分解算法，对复数信号进行分解与重构，以逼近原始发射信号，从而实现更精确的特征提取。进一步采用时序卷积网络建模信号序列特性，并引入空间注意力机制自适应加权关键信号段，显著提升识别性能。此外，分支网络可利用其他数据的预训练权重，减少对辅助数据集的需求。消融实验验证了各模块有效性。在公开数据集上的对比测试表明，本方法仅需10个符号即达到96%的准确率，且无需任何先验知识。
原文摘要 · Abstract (English)
Specific emitter identification (SEI) utilizes passive hardware characteristics to authenticate transmitters, providing a robust physical-layer security solution. However, most deep-learning-based methods rely on extensive data or require prior information, which poses challenges in real-world scenarios with limited labeled data. We propose an integrated complex variational mode decomposition algorithm that decomposes and reconstructs complex-valued signals to approximate the original transmitted signals, thereby enabling more accurate feature extraction. We further utilize a temporal convolutional network to effectively model the sequential signal characteristics, and introduce a spatial attention mechanism to adaptively weight informative signal segments, significantly enhancing identification performance. Additionally, the branch network allows leveraging pre-trained weights from other data while reducing the need for auxiliary datasets. Ablation experiments on the simulated data demonstrate the effectiveness of each component of the model. An accuracy comparison on a public dataset reveals that our method achieves 96% accuracy using only 10 symbols without requiring any prior knowledge.
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