基于TPAM-DPN网络的雷达辐射源识别方法

Radar Emitter Identification with TPAM-DPN Network

针对传统依靠人工经验设计并提取雷达辐射源特征的繁琐和区分度不够的问题,提出了一种改进的双路网络(DPN)自动提取特征并识别的方法。首先将一维的雷达时域信号变换到二维时频域,然后直接输入双路网络进行识别,即将雷达辐射源的识别转化为图像的识别,有效缓解了上述问题。同时,针对双路网络层次过深带来的特征流失问题,提出用于对雷达辐射源特征图校准重采样的轻量级模块——三流注意力模块(TPAM),并嵌入双路网络构成三流注意力双路网络(TPAM-DPN)对雷达辐射源进行识别。对6种常见的雷达信号进行了仿真实验,证明了所提方法提取的特征更有利于提高雷达辐射源识别率,且时效性更好。

The traditional techniques relying on artificial experience for radar emitter feature extraction are cumbersome and have insufficient degree of distinction.To solve the problems,an improved Dual Path Network(DPN)is proposed to automatically extract features and make identification.Firstly,the one-dimensional time-domain signal is transformed into two-dimensional time-frequency domain,and then directly input into the DPN for identification.Thus the identification problem of radar emitter is transformed into an image recognition problem.At the same time,considering the problem of feature loss due to too many network layers in DPN,a Triple Path Attention Module(TPAM)is proposed for re-sampling the radar emitter feature map.Then the TPAM is embedded into DPN to form a TPAM-DPN for identifying the radar emitter.Experiments on six common radar signals show that the features extracted by this method are more conducive to improving the radar emitter identification accuracy and are more time-efficient.