## 10 Conclusions and Roadmap Ahead  
This paper presented a survey of RFF methods for spoofing mitigation in GNSS receivers. While the survey of methods and the methodology presented in here can be generally applied also in a non-GNSS context, the focus in our paper has been on GNSS pre-correlation data, as the pre-correlation anti-spoofing methods are still rare in the current literature. A four-step methodological approach has been proposed in Section 2, by breaking down the RFF problem into several parts: the effects (or features) occurring at the transmitter side, the channel effects, and the receiver effects. We identified the main sources of possible hardware imperfections (i.e., features) at the transmitter side and we introduced in Section 3 detailed mathematical models for the identified HW impairments for GNSS transmitters. It has also been shown that such HW features are best identified with the help of various feature-extraction time-domain or frequency-domain transforms. Some of the most encountered feature-extraction transforms in the current literature were discussed in Section 5. We also surveyed the literature to identify classification algorithms useful in the context of RFF. Several classification methods, both via thresholding and via machine learning algorithms, were addressed in Section 6. Section 8 provided a qualitative comparison of approaches suitable for GNSS pre-correlation data, based on our literature survey, theoretical modelling, and preliminary simulation-based observations. It is to be emphasized that such RFF algorithms need to be further tested via measurement-based data for understanding their full capacity in a realistic environment, but one of the main take-away points of our research has been that the transmitter HW imperfections do have the possibility to act as differentiating features between spoofers and genuine transmitters if proper combinations of feature-extraction transform and classifiers are found. Our focus has been on the transmitter HW features, but we also discussed the possible effects of the wireless channels and the hardware blocks at the receiver side. To sum up, several challenges remain for the roadmap ahead:  
* Addressing the impact of the signal mixtures from signals from various satellites and various frequency bands: typically, the received signal is a mixture of all satellites visible in the sky at the considered moment, and possibly, of one or several spoofing signals. One approach to look at a single signal at a time would be to first despread each signal from each identified pseudo-random code, and then apply successive or parallel interference cancellation methods to identify each signal, one by one. The errors in the estimation of the signals from various satellites would, of course, affect the quality of the re-constructed signal, and possibly, the accuracy of the RFF-based classification. Another approach would be to create huge training databases with all possible mixtures of satellites in the sky and to use those databases in the classification process;
* Evaluating and mitigating the impact of channel multipath and fading effects: each wireless channel (from satellite or spoofer) has its own random signature, determined by the multipath delays, Doppler spreads, and fading effects. As these effects are random in nature, they will, most likely, not provide additional 'features', but will have a negative impact on the strength of the transmitter features.