Wang Wenbo, Aguilar Sanchez Ignacio, Caparra Gianluca, McKeown Andy, Whitworth Tim, Lohan Elena Simona
Electrical Engineering Unit, Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland.
European Space Agency, European Space Research and Technology Centre, 2201 AZ Noordwijk, The Netherlands.
Sensors (Basel). 2021 Apr 25;21(9):3012. doi: 10.3390/s21093012.
Radio frequency fingerprinting (RFF) methods are becoming more and more popular in the context of identifying genuine transmitters and distinguishing them from malicious or non-authorized transmitters, such as spoofers and jammers. RFF approaches have been studied to a moderate-to-great extent in the context of non-GNSS transmitters, such as WiFi, IoT, or cellular transmitters, but they have not yet been addressed much in the context of GNSS transmitters. In addition, the few RFF-related works in GNSS context are based on post-correlation or navigation data and no author has yet addressed the RFF problem in GNSS with pre-correlation data. Moreover, RFF methods in any of the three domains (pre-correlation, post-correlation, or navigation) are still hard to be found in the context of GNSS. The goal of this paper was two-fold: first, to provide a comprehensive survey of the RFF methods applicable in the GNSS context; and secondly, to propose a novel RFF methodology for spoofing detection, with a focus on GNSS pre-correlation data, but also applicable in a wider context. In order to support our proposed methodology, we qualitatively investigated the capability of different methods to be used in the context of pre-correlation sampled GNSS data, and we present a simulation-based example, under ideal noise conditions, of how the feature down selection can be done. We are also pointing out which of the transmitter features are likely to play the biggest roles in the RFF in GNSS, and which features are likely to fail in helping RFF-based spoofing detection.
在识别真正的发射器并将其与恶意或未经授权的发射器（如欺骗器和干扰器）区分开来的背景下，射频指纹识别（RFF）方法正变得越来越流行。在非全球导航卫星系统（GNSS）发射器（如WiFi、物联网或蜂窝发射器）的背景下，RFF方法已经得到了一定程度的研究，但在GNSS发射器的背景下，它们尚未得到太多关注。此外，在GNSS背景下，少数与RFF相关的工作是基于相关后或导航数据的，尚未有作者使用相关前数据来解决GNSS中的RFF问题。此外，在GNSS的背景下，仍然很难找到在三个领域（相关前、相关后或导航）中的任何一个领域的RFF方法。本文的目标有两个：第一，对适用于GNSS背景的RFF方法进行全面综述；第二，提出一种用于欺骗检测的新型RFF方法，重点关注GNSS相关前数据，但也适用于更广泛的背景。为了支持我们提出的方法，我们定性地研究了不同方法在相关前采样的GNSS数据背景下的使用能力，并给出了一个基于理想噪声条件下的模拟示例，展示了如何进行特征降维选择。我们还指出了哪些发射器特征在GNSS的RFF中可能发挥最大作用，以及哪些特征可能无法帮助基于RFF的欺骗检测。
