通过射频指纹识别追踪UWB设备是可行的
Tracking UWB Devices Through Radio Frequency Fingerprinting Is Possible
AI总结：
本文利用现成UWB设备构建真实数据集，并提出改进的深度学习流程提取射频硬件指纹，验证了RFF可用于UWB设备识别与跨环境追踪。
摘要：
超宽带（UWB）是一项为需要厘米级定位的应用而设计的先进技术。它被智能手机制造商广泛采用，自然引发了安全和隐私方面的担忧。将射频指纹识别（RFF）成功应用于UWB，可以实现物理层安全，但也可能允许对设备进行不受欢迎的追踪。本文的范围是探索将RFF应用于UWB的可行性，并研究该技术在不同环境中的泛化能力。我们使用现成的UWB设备，在设备位置受控变化的情况下收集了一个真实数据集。此外，我们开发了一种改进的深度学习流程，用于从信号数据中提取硬件特征。在稳定条件下，提取出的RFF实现了超过99%的准确率。尽管在更易变化的环境中准确率有所下降，但在未经训练的位置上，我们仍获得了高达76%的准确率。
英文摘要：
Ultra-wideband (UWB) is a state-of-the-art technology designed for applications requiring centimeter-level localization. Its widespread adoption by smartphone manufacturer naturally raises security and privacy concerns. Successfully implementing Radio Frequency Fingerprinting (RFF) to UWB could enable physical layer security, but might also allow undesired tracking of the devices. The scope of this paper is to explore the feasibility of applying RFF to UWB and investigates how well this technique generalizes across different environments. We collected a realistic dataset using off-the-shelf UWB devices with controlled variation in device positioning. Moreover, we developed an improved deep learning pipeline to extract the hardware signature from the signal data. In stable conditions, the extracted RFF achieves over 99% accuracy. While the accuracy decreases in more changing environments, we still obtain up to 76% accuracy in untrained locations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
