Radar emitter recognition based on deep learning architecture
Hongbo Li, Jing Wei, Yang Bai · 2016
With the increasing complexity of electromagnetic environment and the rising of operating patterns of new radars, emitter recognition is becoming more and more difficult. This paper presents a deep learning architecture (DLA) based on the deep belief network (DBN) and logistic regression (LR) for radar emitter recognition. A multilayer structure of DBN is established to learn emitter feature, and LR is devoted to identify a specific type of radar. Compared experiments with conventional methods are conducted, and the results show that the proposed model outperforms other existing techniques. Moreover, simulation experiments in different noise and loss pulse environment show that DLA is effective and robust in solving problems of radar emitter recognition.