###### Abstract
Classical ML and transforms methods are used in this four-step methodology, but the four-step block diagram is rather novel;
5. Presenting the mathematical models of different GNSS transmitter features, with a particular emphasis of five main identified features, namely: the power amplifier non-linearities, the digital-to-analog converters' non-linearities, the phase noises of the local oscillators, the I/Q imbalances, and the band-pass filtering at the edge of the transmitter front-end; unified mathematical methods of the transmitter HW impairments are not found in the current literature to the best of the authors' knowledge;
6. Providing the equivalent transmitter block diagrams for GNSS and spoofers by incorporating the aforementioned five hardware effects into the models;
7. Presenting an illustrative simulation-based analysis based under ideal conditions in order to emphasize the impact of each HW feature on the RFF performance. Three feature extractors to identify the transmitter HW impairments were used, namely the kurtosis, the Teager-Kaiser energy operator (TKEO), and the spectrogram. The classification accuracies given as examples are based on support vector machines (SVM). Such a simplified analysis allows us to identify the strongest features among the five considered ones and to point out the remaining challenges to overcome to achieve the feasibility of RFF methods under more realistic GNSS scenarios;
8. Bringing in a qualitative discussion on the existing algorithms and providing a roadmap towards further research on RFF in GNSS for interference detection and classification. The rest of this paper is organized as follows: Section 2 presents the use case of a spoofing attack on an on-board GNSS receiver and describes the various spoofing types and anti-spoofing approaches existing in the literature. It also clarifies the fact that the focus of our paper is on pre-correlation approaches using the I/Q sample-level data as inputs, but the proposed methodology and the identified feature extractors and classifiers can also be applied in a broader sense, with post-correlation and navigation GNSS data, as well as with non-GNSS data. Section 3 gives an overview of the main identified transmitter HW impairments (i.e, 'features'), which can separate between genuine and spoofing transmitters in RFF-based approaches. Section 4 presents the equivalent transmitter block diagrams for GNSS and spoofer signals, by emphasizing the places in the transmission payload where the various RF impairments can appear. This also shows the equivalent block diagram of the whole transmitter-channel-receiver chain and discusses the additional impairments that can be introduced by the channels and the receiver parts. Section 5 focuses on feature-extractor transforms and presents various transforms which can be employed to determine the underlying features in the received signal. Section 6 focuses on classification approaches which can be used to identify the features, after the feature-extractor transform is applied. Section 8 summarizes the main RFF solutions from the existing literature, applied on pre-correlation signals, for both GNSS and non-GNSS signals. Section 9 discusses the methods applicable to GNSS among those listed in Section 8 and offers a qualitative and comparative view of such approaches. Finally, Section 10 summarizes the open challenges in this field as well the further methodological steps to be under-taken for a designer implementing RFF algorithms based on pre-correlation GNSS data.