Deep Learning In Electronic Warfare Systems: Automatic Intra-Pulse Modulation Recognition

Detection and classification of radars in electronic warfare systems is a major problem. In this work, we propose a novel deep learning based method that automatically recognizes intra-pulse modulation types of radar signals. We use reassigned short-time Fourier transforms of measured signals and detected outliers of the phase differences using robust least squares to train a hybrid structured convolutional neural network to distinguish frequency and phase modulated signals. Simulation results show that the developed method highly outperforms the current stateof-the-art methods in the literature.