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.

Radio Frequency fingerprinting (RFF) methods are gaining popularity as physical-layer identification or authentication methods in various navigation and communication applications. Traditionally, RFF has been used in terrestrial communications to identify the genuine transmitters from spoofers and jammers. In recent literature, RFF has gained attention also in the context of satellite navigation and Low Earth Orbit (LEO) satellite communications, though this research area is still in an incipient phase. RFF studies in the context of satellite transmitters (or transceivers) are typically hindered by the challenges in acquiring high-quality raw measurement data. In this paper, we analyze via RFF methods, in both pre-correlation and post-correlation domain, the raw GNSS data collected at three locations: Tampere (Finland), Nottingham (UK), and Nuremberg (Germany). The datasets for the first two scenarios have been collected by the authors, while the third dataset is available in open access. We show that we are able to reach average classification probabilities of spoofer versus GNSS up to 99.99% (i.e., Nuremberg measurements) with pre-correlation data and up to 87.72 % (i.e., Nottingham measurements) with post-correlation data. We also discuss the challenges and limitations of RFF in the context of GNSS.

The aim of this paper is to study the performance of some coding schemes recently proposed for updating the TC channel coding standard for space applications, in the presence of jamming. Besides low-density parity-check codes, that appear as the most eligible candidates, we also consider other solutions based on parallel turbo codes and extended BCH codes. We show that all these schemes offer very good performance, which approaches the theoretical limits achievable.