Akyön, Fatih ÇağatayAlp, Y. K.Gök, G.Arıkan, Orhan2019-02-212019-02-2120189781538615010http://hdl.handle.net/11693/50216Date of Conference: 2-5 May 2018Detection 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 state-of-the-art methods in the literature.TurkishConvolutional neural networksDeep learningElectronic warfareRadar modulation recognitionRobust least squaresTime-frequency imageConference Paper10.1109/SIU.2018.8404294