###### Abstract
In order to address this research gap of how to apply the well-known radio frequency fingerprinting and ML methods (to date widely used in other research fields) in the context of GNSS receivers, we present here a comprehensive survey of RFF and ML methods, discuss their applicability in the GNSS context, and we introduce a novel methodology to deal with RFF in GNSS, by presenting equivalent block diagrams of the genuine and non-genuine GNSS transmitters. We also give an initial glimpse of what kind of transmitter features are the most important in the context of GNSS transmitters, based on an in-house-made simulator, with Matlab and Python modules. We further summarize the remaining challenges when dealing with realistic environments and point out a few possible paths for future research in this challenging field. A schematic block diagram of the three domains (pre-correlation, post-correlation, and navigation) of a typical GNSS receiver is shown in Figure 1. The pre-correlation domain refers to the data at the output of the Automatic Gain Converter (AGC) and Analog-to-Digital Converter (ADC) shown in Figure 1, in other words, to the raw I/Q samples before the acquisition stage of the GNSS receiver. These samples are typically received at a very low signal-to-noise-ratio, but they can carry important information about the 'features' of the transmitter, as they are not yet smoothed or filtered with the correlation filters. Figure 1: The three domains of a typical GNSS receiver: pre-correlation; post-correlation; and navigation domains. A good survey of anti-spoofing methods based on the post-correlation and navigation data in GNSS can be found for example in [19]. However, no pre-correlation methods and no RFF methods were addressed in there. Others surveys of anti-spoofing methods can be found for example in our previous work in [11; 20], where again only the post-correlation and navigation anti-spoofing solutions were addressed. Feature-selection methods for RFF based on the navigation domain of a GNSS signal have also been addressed in [21]. Surveys on the RFF methods are more difficult to find in the current literature, and they are typically focused on non-GNSS signals, such as cellular, Internet of Things (IoT), or WiFi signals [22; 23; 24; 25; 26]. As seen in the discussions above, there is still a lack of surveys of RFF methods for GNSS transmitter authentication in the current literature, particularly on surveys of GNSS authentication relying on pre-correlation signals. In this paper, we are addressing this lack, via a comprehensive study of the literature in the past two decades, as well as via theoretical insights and the preliminary analysis of algorithms. Our contributions are as follows:  
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2. Offering a thorough survey of RFF methods applied with GNSS and non-GNSS wireless data in the literature, and discussing which of these RFF methods have potential in GNSS, and in particular in GNSS with pre-correlation data. Finding good anti-spoofing methods based on pre-correlation GNSS data could have tremendous benefits for the future GNSS receivers, by being able to detect and remove non-genuine signals even before processing them further in the acquisition and tracking loops. Our survey is unique in the current literature, as the RFF methods for GNSS have to date not been widely investigated and there is a current lack of unified surveys on this;
3. Proposing a step-by-step problem definition of RFF in the context of GNSS signals, by delving in depth in the sources of possible transmitter hardware impairments, and also discussing the possible channel and receiver-hardware impairments; this problem decomposition into feature-by-feature investigation is also lacking from the current GNSS literature, to the best of our knowledge;
4. Proposing a four-step generic RFF approach, consisting of: feature identification, feature extraction, data pre-processing, and data classification.