Tracking UWB Devices Through Radio Frequency Fingerprinting Is Possible

TL;DR

This paper investigates the feasibility of using Radio Frequency Fingerprinting to identify UWB devices, achieving high accuracy in stable environments and reasonable accuracy in changing conditions, thus enabling potential security and privacy applications.

Contribution

It demonstrates the application of deep learning-based RFF to UWB devices and evaluates its effectiveness across different environments.

Findings

Over 99% accuracy in stable conditions

Up to 76% accuracy in untrained locations

Deep learning pipeline effectively extracts device signatures

Abstract

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.