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摘要: 由于人类活动产生的电磁干扰与真实的雷电电磁脉冲信号在时域和频域上高度混叠，如何高效、准确地区分雷电电磁信号与非雷电电磁信号，已成为雷电监测预警及灾害防御领域的关键问题。针对雷电电磁信号与人为干扰信号在波形形态上高度相似、对其诊断识别难度大的问题，本文提出了一种基于多尺度残差卷积与不同网络层特征融合相结合的深度神经网络模型CNN-LSTM (Convolution Neural Network and Long-short Term Memory)，用于雷电与非雷电电磁信号的二分类任务。通过多尺度残差网络模型逐层提取探测设备接收到的电磁波中的多维度特征，将各卷积层输出的时域特征按照网络层深度的次序，构建为一个跨网络层的时域特征序列，并输入长短期记忆网络(LSTM)中进行自适应加权融合，该机制利用LSTM对序列信息的建模能力，学习不同层级特征的相对重要性，而非建模原始波形的时间动态。实验结果表明，所提诊断识别方法在真实雷电观测数据集上表现出优异的分类性能：其对雷电电磁信号的识别精确率达到100%，召回率为99.82%，F1得分为99.91%，整体准确率达99.89%。与多种经典基线模型相比，本文提出的CNN-LSTM模型不仅能高效地识别出雷电样本，还能显著降低对非雷电干扰信号的误报率。此外，消融实验进一步验证了CNN网络在局部特征提取以及 LSTM 在跨层特征融合中的关键作用，证明了所提出架构的合理性与有效性。
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关键词:
- 雷电电磁脉冲信号 /
- 深度学习 /
- 卷积神经网络CNN /
- 长短期记忆网络LSTM /
- 特征融合
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表 1 CNN-LSTM及基线模型性能对比
模型 Precision Recall F1得分 Acc Bayes 93.14% 80.14% 86.15% 84.89% SVM 93.79% 86.4% 89.94% 88.67% MLP 97.5% 90.7% 93.98% 93.18% KNN 95.16% 87.84% 91.35% 90.24% DT 96.85% 88.01% 92.22% 91.29% CNN-LSTM 100% 99.82% 99.91% 99.89% 
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