## 6 RF Feature Classifiers  
Feature classification methods can be typically split into two main classes: (i) methods based on thresholding or the direct sorting of the outputs of the feature extraction stage; and (ii) methods based on machine learning (ML) classifiers. The second category was by far the category most encountered in RF fingerprinting, as shown previously in Table 1.  
Figure 12: An example of spectrogram-based feature extraction. The left-hand figure is a spectrogram of genuine GNSS (Galileo E1) transmitter, the right-hand figure is a spectrogram of spoofer (Galileo E1) transmitter.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref., Year** & **Studied Signal Types** & **Studied Algorithms** & \
\begin{tabular}{c} **Detection Performance** \\ **Metrics Given2** \\ \end{tabular} &
\begin{tabular}{c} **Using IQ for** \\ **Pre-Correlation Data7** \\ \end{tabular} & **Domain** \\ \hline
[62], 2003 & Bluetooth and WiFi & Bayesian step detector of transients & No & Yes & IoT \\
[63], 2006 & Ethernet devices & Matched filtering & No & No & Cable networks \\
[60], 2007 & Chipcon sensors at 433 MHz carrier & DWT & No & Yes & IoT \\
[64], 2008 & WiFi & Support vector machines (SVM) and CNN & No & Yes & IoT \\ \hline
[65], 2009 & QPSK and DQPSK modulated & Maximum likelihood classification & No & Yes & IoT \\
[66], 2010 & WiFi and GC/LTE & Analysis of variance (ANOVA) classification & No & Yes & Cellular \\
[66], 2012 & TDMA satellites with QPSK modulation & SDA & No & Yes & Satcomm \\
[68], 2014 & 16-APSK modulated narrowband signal & Analytical study & No & No & IoT \\
[67], 2015 & UWB noise radar & MDA & Yes & No & Radar \\
[68], 2003 & **GNSS** & Allan deviation and time interval error & Yes & No & GNSS \\
[69], 2017 & nRF24 LU1+ IoT devices at 24 GHz & Permutation entropy (FE) and dispersion & Yes & Yes & IoT \\  & & entropy (DE) with SVM & Yes & Yes & IoT \\
[60], 2017 & GMSK-modulated narrowband signals & Normalized FE & No & Yes & IoT \\
[61], 2017 & **GNSS** & Allan deviation and time interval error & Yes & No & GNSS \\
[66], 2017 & WiFi & Probabilistic neural network (PNN) classifier & No & Yes & IoT \\
[66], 2018 & **GNSS** & Polarization vector with dual antennas & No & No & GNSS \\
[67], 2019 & Cellular signals & Kurtosis & No & Yes & Cellular \\
[68], 2019 & GSM & Continuous wavelet transform (CWT) and CNN & Yes & Yes & Cellular \\ \hline
[69], 2019 & IoT amplifiers & Linear discriminant analysis (LDA) & Yes & Yes & IoT \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of state-of-the-art: RFF-related studies based on pre-correlation data, for wireless communications, and navigation applications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref., Year** & **Studied Signal Types** & **Studied Algorithms** & **Detection Performance Metrics Given2** & **Using IQ/Qor Pre-Correlation Data?** & **Domain** \\ \hline
[74], 2019 & AM-modulated signal & CNN & Yes & Yes & IoT \\ \hline
[75], 2019 & QPSK-modulated narrowband signals & Hilbert-Huang Transform (HHT) and CNN & Yes & Yes & IoT \\ \hline
[76,77], 2020 & ADS-B signals & CNN & Yes & Yes & Aviation \\  & & & & (surveillance) \\ \hline
[78], 2020 & UAV controller & SVM, random forest, neural networks & Yes & Yes & Aviation (UAVs) \\ \hline
[79], 2020 & ADS-B signals &
\begin{tabular}{c} CNN, message structure aided attentional \\ convolution network (MSACN) \\ \end{tabular} & Yes & Yes & Aviation \\ \hline
[80], 2020 & Wimax transmitters & SVM & Yes & Yes & IoT \\ \hline
[81], 2020 & UAV transmitters & Neural networks & Yes & Yes & Aviation (UAVs) \\ \hline
[82], 2021 & ZigBee signals & Gaussian probabilistic LDA & Yes & Yes & IoT \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cort._