Embedding Bottleneck Gated Recurrent Unit Network for Radar Signal Recognition

Abstract

Radar signal recognition plays an significant role in civil applications. Corresponding to two types of intentional modulation signal and unintentional fingerprint signal, radar signal recognition has two kinds of tasks—automatic modulation classification and radar emitter identification. In this paper, we propose a Embedding Bottleneck Gated Recurrent Unit (EBGRU) network that can handle these two tasks separately. The EBGRU consists of three main processing steps. Firstly, the normalized signal pulses are trained in pulse embedding network containing several embedding methods: Pulse2Vec, GloVeP and EPMo, during which we regard the radar signal pulses as radar signal-linguistic sequences for the first time. Then, pulses embeddings are added to original pulses and are sampled to form latent representations of pulses through information bottleneck. Finally, the gated recurrent unit network is utilized to predict radar signal labels. Experiment results show that the proposed method has reached 95.33% on simulated modulation signals and 94.67% at real intercepted emitter signals with relatively less network parameters.