Radar Emitter Identification Based on Deep Learning of Inter-pulse Features

Under the complex program scheduling, the multi-function radar has the characteristics of wide value range, fast agility, and strong randomness. It is difficult for non-cooperative receivers to establish an effective model of the signal, which brings serious challenges to the radar radiation source identification of electronic reconnaissance systems. This paper proposed a complex system radar emitter identification method based on deep learning, which used full pulse data of a large sample to form an image feature representation of pulse-to-pulse parameter changes, macroscopically revealed the waveform design mechanism implied by the radar radiation source, and designed a deep learning network of image features based on AlexNet to carry out radiation sources identification. The measured data experiments show that the algorithm has good recognition performance for several multifunctional radars of the same type within a certain time span, which provides a new solution to the intelligent identification of multi-function radar emitter.