| 摘要: |
| 针对现代战场电磁环境下复杂调制雷达信号分选准确率低问题，本文提出一种基于密集连接卷积网络(Densely Connected Convolutional Networks, DenseNet)的雷达辐射源信号分选算法。该算法采用脉冲描述字(Pulse Description Word, PDW)参数与脉内参数相结合作为分选特征，并对特征参数进行差值预处理生成训练数据，使用一维DenseNet进行分选。采用本文预处理方法可以有效提取特征间的相关性差异，同时弥补脉间参数PDW对脉内调制信息的缺失。实验结果表明，该方法能有效实现复杂雷达辐射源信号的分选，在0dB的信噪比下可以达到98%以上的分选准确率。 |
| 关键词: 雷达信号分选 脉间特征 脉内特征 密集神经网络 |
| DOI： |
| 分类号:TN971 |
| 基金项目: |
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| Radar Emitter Signal Sorting Based on Densely Connected Convolutional Networks |
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| Abstract: |
| To address the problem of low accuracy in complex modulated radar signal sorting in modern battlefield electromagnetic environments, this paper proposes a radar emitter signal sorting algorithm based on Densely Connected Convolutional Networks (DenseNet). The algorithm uses the combination of Pulse Description Word (PDW) parameters and intra-pulse parameters as the sorting feature, preprocesses the feature parameters to generate training data, and uses one-dimensional DenseNet for sorting. The proposed preprocessing method helps extract correlation differences between features and compensate for the lack of intra-pulse modulation information caused by the inter-pulse parameter PDW. The experimental results show that this method can effectively realize the sorting of complex radar emitter signals, and the sorting accuracy can reach more than 98% under the signal-to-noise ratio of 0 dB. |
| Key words: Radar signal sorting Interveinal characteristics Intravenous characteristics DenseNet |