The Global Positioning System (GPS) is not only widely used in navigation, measurement and other services, but also an indispensable key equipment for the military. With the increasing complexity of the communication environment and the increasing number of interference factors, the recognition of GPS interference signal types is a prerequisite for the development of efficient anti-interference means. This paper focuses on three typical GPS interference signals, by extracting four different entropy features including power spectral entropy, establishing a hybrid entropy dataset and then using support vector machine (SVM) and random forest (RF) methods so as to classify and identify the dataset. The results show that the RF has a high recognition rate for the interference signal, and the average accuracy is above 90%, which greatly exceeds the SVM. Also, in the three kinds of interference signals, the noise FM interference is the least concealed and the most easily recognized.

全球定位系统（GPS）不仅广泛用于导航，测量和其他服务，而且还是军事上必不可少的关键设备。随着通信环境的复杂性增加以及干扰因素的数量增加，对GPS干扰信号类型的识别是开发有效的抗干扰手段的先决条件。本文着重研究三种典型的GPS干扰信号，方法是提取包括功率谱熵在内的四个熵特征，建立混合熵数据集，然后使用支持向量机（SVM）和随机森林（RF）方法对数据集进行分类和识别。 。结果表明，射频对干扰信号的识别率很高，平均准确率在90％以上，大大超过了支持向量机。