Radar Waveform Recognition using Fourier-Based Synchrosqueezing Transform and CNN

In this paper the problem of recognizing radar waveforms is addressed. Waveform classification is needed in spectrum sharing and radar-communications coexistence, cognitive radars and signal intelligence. Different radar waveforms exhibit different properties in time-frequency domain. We propose a deep learning method for waveform classification. The received signal is processed with Fourier synchrosqueezing transform that has excellent properties in revealing timevarying behavior, rate of, strength and number of oscillatory components in received signals. The resulting time-frequency description is represented as a bivariate image that is fed into a convolutional neural network. The proposed method has superior performance over the widely used the Choi-Williams distribution (CWD) method in distinguishing the polyphase waveforms even at low signal-to-noise ratio regime.