基于IQ图特征的小样本通信辐射源个体识别

在真实的战场环境中，我们很难采集到足够的带标签的敌方辐射源数据，因此，小样本学习变得越来越重要。通过不断地发展，CNN神经网络有着很强的处理图片分类的能力。在小样本条件下，为了充分利用发展最为成熟的CNN神经网络，本文提出了将一维IQ数据转化成二维的IQ图特征的方法，来进行针对小样本的分类任务。由于数据的IQ图具有重复性与个体的差异性，通过实验，这种方法在识别不同个体超短波电台上有着99.5%的正确率，对比双谱特征，IQ图特征具有更强的识别能力。这种特征变换方法简单，并且CNN网络处理图片分类的技术成熟，具有很强的实用性。 

Specific Emitter Identification of small sample comvmmunication radiation source based on IQ graph features

In a real battlefield environment, it is impossible for us to collect enough local radiation source data. Small sample learning becomes more and more important. Through continuous development, CNN neural networks have a strong ability to process image classification. Under the condition of small samples, in order to use the most mature CNN neural network, this paper proposes a method of converting one-dimensional IQ data into two-dimensional IQ graph features to perform classification tasks for small samples. Due to the repeatability of the IQ map of the data and the difference of the individual, this method has 99.5% accuracy in identifying different individuals on ultrashort wave radio stations. Compared with the bispectrum feature, the IQ map feature has a strong generalization ability. This method has a simple feature transformation, and the CNN network has a mature technology for processing picture classification, which has strong practicality.