SepNet: Deep Convolutional Neural Network for Specific Emitter Identification with High Accuracy

Abstract

Specific emitter identification refers to identifying a specific emitter by its radio frequency fingerprint extracted from a given signal. Recently, most current methods for specific emitter identification are usually based on neural networks due to their great success. However, with the increasing complexity of radar systems, the neural networks of these methods are too shallow to extract distinctive fingerprint characteristics of modern emitters, especially for those of the same type, resulting in low identification accuracy. To address this challenge, this paper proposes a novel deep convolutional neural network named SepNet. We observed that the feature representation of each radar signal can be decomposed into two independent parts: a radio frequency fingerprint part capturing features of the specific emitter and a variation part capturing features of the signal content. Based on this insight, SepNet is designed to separate fingerprint information from raw signals and uses it to identify specific emitters with high accuracy. SepNet incorporates a separation block and a classification block to perform feature separation and emitter identification tasks, respectively. In addition, a reconstruction block is instrumented into SepNet to guide the separation process. Experimental results demonstrate that SepNet outperforms four end-to-end deep learning models in terms of identification accuracy, and ablation studies confirm the effectiveness of SepNet’s architecture.