标题： 通过混合数据增强和深度度量学习的少样本特定发射机识别 显示英文标题

标题： Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric Learning

摘要： 特定发射机识别（SEI）是一种潜在的物理层认证技术，它是上层认证最重要的补充之一。 基于射频指纹（RFF）的SEI旨在通过电子组件的不变射频特性来区分不同的发射机。 由于深度学习（DL）具有强大的提取隐藏特征和执行分类的能力，它可以从前大量信号样本中提取高度可分离的特征，从而实现SEI。 考虑到训练样本有限的条件，我们提出了一种基于混合数据增强和深度度量学习（HDA-DML）的新颖的少样本SEI（FS-SEI）方法，该方法摆脱了对辅助数据集的依赖。 具体来说， 设计了包含旋转和CutMix的HDA以增加数据多样性，并使用DML提取高判别语义特征。 所提出的基于HDA-DML的FS-SEI方法在一个开源的大规模真实世界的自动相关监视广播（ADS-B）数据集和一个真实世界的WiFi数据集上进行了评估。 两个数据集的仿真结果显示，所提出的方法比五种最新的FS-SEI方法实现了更好的识别性能和更高的特征判别能力。 显示英文摘要

摘要： Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication. Radio frequency fingerprint (RFF)-based SEI is to distinguish one emitter from each other by immutable RF characteristics from electronic components. Due to the powerful ability of deep learning (DL) to extract hidden features and perform classification, it can extract highly separative features from massive signal samples, thus enabling SEI. Considering the condition of limited training samples, we propose a novel few-shot SEI (FS-SEI) method based on hybrid data augmentation and deep metric learning (HDA-DML) which gets rid of the dependence on auxiliary datasets. Specifically, HDA consisting rotation and CutMix is designed to increase data diversity, and DML is used to extract high discriminative semantic features. The proposed HDA-DML-based FS-SEI method is evaluated on an open source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset and a real-world WiFi dataset. The simulation results of two datasets show that the proposed method achieves better identification performance and higher feature discriminability than five latest FS-SEI methods.