Recognition of GPS interference signals based on neural network and SVM
Zhenming Feng · Information and Electronic Engineering · 2009
Global Positioning System(GPS) interference recognition is the prerequisite of effective anti-interference measures.In this study,eight features,including higher-order statistics,were extracted,and Back Propagation(BP) neural network classifier and polynomial Support Vector Machine(SVM) classifier were employed for recognition of seven typical types of GPS interference signals.Simulation results demonstrated that both classifiers performed well with high recognition rates and robustness when thermal noise existed,and when Jammer-To-Noise Ratio(JNR) was 3 dB,the average recognition rates still remained above 94%.