End-to-End Learning for Radar Electronic Support: Multi-label Classification and Explainable AI

The detection and classification of radar emitters from the electromagnetic spectrum is an essential task for electronic support. This article introduces an innovative method that directly estimates the radar emitters from the spectrogram of received data in a single step, utilizing a convolutional neural network. The processing does not rely on the typically used sequence of pulse detection, pulse deinterleaving, and emitter identification and does not require the creation of pulse descriptor words. The approach can detect multiple interleaved emitters in a spectrogram and is, therefore, a multilabel classification task. The feasibility of this single-step approach is evaluated for a library of 50 emitters, which includes high parameter agility. The results show that the proposed approach provides a high detection probability of up to 98 % while maintaining a low false alarm rate of only 0.02%. In addition, we propose an explainable artificial intelligence technique based on occlusion that is well suited to radar emitter recognition in spectrogram data. A final evaluation of the trained neural network with test signals generated in the lab investigates the robustness to some real-world radio frequency effects.