GPS Interference Signal Recognition Based on Machine Learning

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.