一种基于Swin Transformer神经网络的低截获概率雷达信号调制类型的识别方法

本文针对低截获概率（Low Probability of Intercept, LPI）雷达信号调制类型的识别问题提出了一种基于Swin Transformer神经网络的识别方法. 该方法首先用平滑伪Wigner-Ville分布对信号进行时频变换，将一维时域信号变换为二维时频图像，然后使用Swin Transformer神经网络对图像进行特征提取及调制类型识别. 仿真结果显示，该方法具有较强的抗噪声能力，在低信噪比条件下识别准确率高，且具有较强的小样本适应能力.

A radar signal modulation type recognition method based on Swin Transformer neural network

In this paper, based on the Swin Transformer neural network we propose a method for the recognition of modulation type of the Low Probability of Intercept (LPI) radar signal. We firstly perform the time-frequency transformation, that is to say, transform the one-dimensional time-domain signal into a two-dimensional time-frequency image by using the smooth pseudo Wigner-Ville distribution. We then use the Swin Transformer neural network to extract the desired features from the image and identify the modulation type. Numerical simulation shows that this method processes strong anti-noise ability and high recognition accuracy even under the condition of low signal-to-noise ratio. Meanwhile, it is also shown that this method has strong adaptability to small samples.