End-to-End Recognition of Interleaved Radar Emitters from the Spectrogram

The application of electronic support to identify the presence of radar emitters is vital to achieve situational awareness in the electromagnetic spectrum. This paper presents a novel approach to electronic support, that estimates the present radar emitters directly from the spectrogram of recevied IQ data in a single step using 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 (PDWs). The feasibility of this monolithic approach is investigated for a library of 50 emitters, that are interleaved and include high parameter agility. The results show, that the proposed approach provides a high probability of detection of up to 98 % while maintaining a low false alarm rate of only 0.02 %.