Transformer-based models for intrapulse modulation recognition of radar waveforms

The increasing prevalence of low probability of intercept (LPI) radars in electronic warfare (EW) systems highlights the need to effectively recognize phase-coded radar waveforms intercepted at radar warning receivers (RWRs) from various threat emitters. The complexities of the electromagnetic (EM) spectrum necessitate the implementation of an automatic modulation recognition system (AMRS) within the RWR. However, a major challenge is accurately identifying phase-coded waveforms with high accuracy at low signal-to-noise ratios (SNRs). This research addresses the challenge by exploring three artificial intelligence (AI)-driven AMRS architectures for identifying phase-coded waveforms using short-time Fourier transform (STFT): vision transformer (ViT), vicinity vision transformer (VViT), and deep convolutional neural network (DCNN). Unlike recent methods focusing on amplitude spectra, our research delves into the phase spectra for the feature extraction of phase-coded waveforms. We leverage phase-based features extracted from intercepted phase-coded waveforms to classify six types of phase-coded signals using these AMRS architectures across SNR levels ranging from −16 dB to 8 dB. The simulation experiments show that these methods are effective at an SNR of −16 dB, with VViT and ViT achieving recognition accuracies of 93% and 92.7%, respectively. Both outperform the DCNN, which achieves an RA of 89% at the same SNR. This approach promises to enhance situational awareness and decision-making in EW operations by improving phase-coded radar waveform recognition and enabling appropriate countermeasure deployment.