Automatic Modulation Classification Exploiting Hybrid Machine Learning Network

Abstract

It is a research hot spot in cognitive electronic warfare systems to classify the electromagnetic signals of a radar or communication system according to their modulation characteristics. We construct a multilayer hybrid machine learning network for the classification of seven types of signals in different modulation. We extract the signal modulation features exploiting a set of algorithms such as time-frequency analysis, discrete Fourier transform, and instantaneous autocorrelation and accomplish automatic modulation classification using naive Bayesian and support vector machine in a hybrid manner. The parameters in the network for classification are determined automatically in the training process. The numerical simulation results indicate that the proposed network accomplishes the classification accurately.