Interference Recognition Based on Machine Learning for Satellite Communications
Hang Yu, Rui Zhang, Rui Ding · 2018
This paper proposes an interference recognition technique based on machine learning for satellite communications. The aim of this paper is to improve the accuracy of interference recognition through machine learning methods. First, we use the structure of convolutional neural network (CNN) to extract features from different kinds of interference signal, then we reduce the dimensionality of features through the technique of multidimensional scaling(MDS). At last we pass the features to the support vector machine(SVM) and get the classification result. The result of the experiments shows that the proposed technique can achieve an excellent classification precision.