提出通用机器学习框架，自动提取无线信号指纹用于识别发射源。
A Generic Machine Learning Framework for Radio Frequency Fingerprinting
- 基于通用机器学习框架，无需针对特定发射器设计
- 在卫星监视、反无人机等真实数据集上验证有效
- 适用于发射源识别、数据关联与聚类等多种任务
无线电频率（RF）发射源指纹提取通常依赖于接收信号中独特的特征。这些指纹细微但足够详细，推动了高效提取方法的研究。最关键的下游任务是特定发射源识别（SEI），即识别每个独立发射器。RFF和SEI具有悠久历史，广泛应用于信号情报、电子监视、无线设备物理层认证等领域。近年来，数据驱动的RFF方法因能自动学习复杂指纹而流行，相比传统方法（往往耗时、僵化、仅适配特定发射器或传输方案）性能更优。本文提出一个通用且灵活的机器学习框架，支持多种下游任务，如SEI、数据关联（EDA）和射频发射源聚类（RFEC），具备发射器类型无关性。我们在空间监视、信号情报和反无人机等真实射频数据集上展示了该框架的适用性与有效性。
原文摘要 · Abstract (English)
Fingerprinting radio frequency (RF) emitters typically involves finding unique characteristics that are featured in their received signal. These fingerprints are nuanced, but sufficiently detailed, motivating the pursuit of methods that can successfully extract them. The downstream task that requires the most meticulous RF fingerprinting (RFF) is known as specific emitter identification (SEI), which entails recognising each individual transmitter. RFF and SEI have a long history, with numerous defence and civilian applications such as signal intelligence, electronic surveillance, physical-layer authentication of wireless devices, to name a few. In recent years, data-driven RFF approaches have become popular due to their ability to automatically learn intricate fingerprints. They generally deliver superior performance when compared to traditional RFF techniques that are often labour-intensive, inflexible, and only applicable to a particular emitter type or transmission scheme. In this paper, we present a generic and versatile machine learning (ML) framework for data-driven RFF with several popular downstream tasks such as SEI, data association (EDA) and RF emitter clustering (RFEC). It is emitter-type agnostic. We then demonstrate the introduced framework for several tasks using real RF datasets for spaceborne surveillance, signal intelligence and countering drones applications.
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