###### Abstract  
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. Global navigation satellite systems (GNSS); spoofing; radio frequency fingerprinting (RFF); I/Q (pre-correlation) data; support vector machines (SVM); classifiers; feature extractors +
Footnote †: journal: _Sensors_  
ones. For example, the authors in [10] performed a thorough analysis of GPS signals using a 30 m dish antenna, illustrating the evolution of the signal quality among the different GPS satellite generations. The paper indirectly showed that with a sufficiently high gain antenna, if the signal-to-noise ratio (SNR) is sufficiently improved, it is possible to identify the specific GNSS signal transmitter. Especially in the context of global navigation satellite systems (GNSS), intentional interference such as jamming and spoofing has been on the rise in recent years and can have significant adverse effects on the navigation performance of GNSS receivers, as discussed for example in [11; 12; 13; 14; 15]. Future aviation applications, and in particular unmanned aerial vehicles (UAVs), will increasingly rely on GNSS-based navigation and positioning solutions [14; 15]. Safety-critical applications, such as those from the aviation domain, require a high capability of anti-spoofing and anti-jamming detection, or, in other words, a high identification accuracy of genuine and malicious transmitters. There are many authentication and anti-spoofing methods in GNSS which are not based on RFF and such methods that have been widely studied in post-correlation, and especially at navigation levels [11; 16; 17; 18; 19]. Recently, with the advent of RFF concepts in many non-GNSS wireless communications and with increased capabilities of machine learning (ML) approaches, the RFF solutions have also started to be considered in the GNSS field; in particular, the research problem of whether RFF could work with raw GNSS data, in the pre-correlation domain, before acquisition and tracking, remains an unsolved problem. It is the purpose of this paper to shed more light on whether RFF on pre-correlation GNSS data can work and which are the challenges and limitations in this field.