基于SE-ResNeXt网络的低信噪比LPI雷达辐射源信号识别

针对低信噪比(signal to noise ratio, SNR)低截获概率(low probability of intercept, LPI)雷达脉内波形识别准确率低的问题，提出一种基于时频分析、压缩激励(squeeze excitation, SE)和ResNeXt网络的雷达辐射源信号识别方法。首先通过Choi-Williams分布(Choi-Williams distribution, CWD)获得雷达时域信号的二维时频图像(time-frequency image, TFI)；然后进行TFI预处理降低噪声干扰和频率维的位置分布差异，以适应深度学习网络输入；最后在ResNeXt基础上加入扩张卷积和SE结构提取TFI特征，实现雷达辐射源分类。实验结果表明，SNR低至-8 dB时，该方法对12类常见LPI雷达波形的整体识别准确率依然能达到98.08%。

LPI radar emitter signals recognition in low SNR based on SE-ResNeXt network

Aiming at the problem of low signal to noise ratio (SNR) and low probability of intercept (LPI) radar pulse waveform recognition accuracy, a radar emitter signal recognition method based on time-frequency analysis, squeeze-excitation (SE) and ResNeXt network is proposed. Firstly, the radar time domain signal is transformed into a two-dimensional time-frequency image (TFI) by Choi-Williams distribution (CWD); then, the TFI pre-processing is used to reduce the noise interference and the difference in frequency dimension location distribution, adapting to deep learning network input; finally, the TFI features are extracted by adding dilated convolution and SE structure on the basis of ResNeXt to achieve radar emitter classification. The experimental results show that when the SNR is as low as -8 dB, the overall recognition accuracy of the method for 12 types of common LPI radar waveforms can still reach 98.08%.