A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data
Wang, Wenbo et al. “A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data.” Sensors (Basel, Switzerland) 21 (2021): n. pag.
This paper presented a survey of Radio Frequency Fingerprinting (RFF) methods for spoofing mitigation in Global Navigation Satellite Systems (GNSS) receivers, with more focus on GNSS pre-correlation data. The radio frequency fingerprinting (RFF) concept refers to the process of identifying the hardware (HW) characteristic and HW-specific features or signatures embedded in the radio frequency (RF) waves transmitted over a wireless channel. In a broader sense, the RFF process has been studied in the context of transmitter-specific HW features, channel characteristics, and joint transmitter-receiver identification (i.e. the effects occurring at the transmitter side, the channel effects, and the receiver effects). The paper offered a thorough survey of RFF methods applied to GNSS and non-GNSS wireless data and stated that “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”. The authors proceed to identify the source of possible channel, transmitter and receiver hardware impairments and proposed a four-step RFF methodology approach to be adapted by a designer wishing to build an RFF algorithm in GNSS receivers. The approach consists of feature identification, features extraction, data pre-processing, and data classification. The HW features are best identified with the help of various feature-extraction time-domain or frequency-domain transforms, and the authors discussed some of the most encountered feature-extraction transforms in the literature as well as surveyed classification algorithms used in the context of RFF, including Thresholding and ML algorithms. One of the main take-away points from this survey is that the transmitter HW imperfections do have the possibility to act as differentiating features between spoofers and genuine transmitters if proper combinations of features-extraction transform and classifiers are found. The identified transmitter HW features are likely to be reflected not only in the pre-correlation data, but also in the post-correlation and navigation domains. Thus, the four-step methodology paves the road towards more advanced RFF GNSS processing in all three domains, with future aim to offer robust and hybrid anti-spoofing solutions.