# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 2 Background to Interference Monitoring
### Detection  
The first stage of interference monitoring is to detect interferences. There are several methods to detect interference, and they are grouped into two general categories. First, GNSS receiver outputs monitoring considers the interference's impact on GNSS processing. Second, the raw in-phase and quadrature-phase (IQ) signal is extracted and often extended to spectrum monitoring to directly measure the interference signals for detection. Monitoring the GNSS receiver carrier-to-noise density ratio (CN0) is a popular approach to determine the interference impact, as the CN0 reduces when an interference signal is present. The spectral separation coefficient (SSC) equation determines the impact of interference on the CN0 [33; 37; 38; 39]:  
\[\left(C/N_{0}\right)_{\text{eff}}=\frac{1}{\frac{1}{C/N_{0}}+\frac{I/C}{Q_{ \text{j}}\cdot R_{\text{c}}}} \tag{1}\]  
where \(\left(C/N_{0}\right)_{\text{eff}}\) is the CN0 impacted by the interference, \(C/N_{0}\) is the interference-free measure, \(I/C\) is the interference-to-signal ratio, \(R_{\text{c}}\) is the chipping rate of the GNSS signal, and \(Q_{\text{j}}\) is the jamming resistive quality factor of the interference signal. The jamming resistive quality factor \(Q_{\text{j}}\) is a measure of the interference signal impact and relates to the power spectral density (PSD) of the GNSS signal \(S_{\text{s}}(f)\) and the interference signal \(S_{\text{i}}(f)\):  
\[Q_{\text{j}}=\frac{\int_{-\beta/2}^{\beta/2}\left\|H(f)\right\|^{2}S_{\text{ s}}(f)\,\text{d}f}{R_{\text{c}}\int_{-\beta/2}^{\beta/2}\left\|H(f)\right\|^{2}S_{ \text{s}}(f)\,S_{\text{i}}(f)\,\text{d}f} \tag{2}\]  
where \(H(f)\) is the front end response of the receiver, and \(\beta\) is the receiver bandwidth. Equation (1) clearly shows that the GNSS signal strength, the interference signal strength, and the interference waveform significantly impact the CN0. The lower the jamming resistive quality factor \(Q_{\text{j}}\) is, the more efficient the interference signal is, as a lower interference-to-signal ratio (ISR) is required for the same degradation. Simply detecting low CN0 values is invalid, as the CN0 may be suppressed for other reasons, such as signal obstruction (e.g., indoor reception), multipath (i.e., signal distortion),broken equipment (e.g., the antenna is damaged or not connected), or a low-elevation satellite (i.e., significant signal atmospheric and antenna suppression). Valid detection approaches include: comparing the measured CN0 directly with the expected CN0, comparing the CN0 to measurements from other GNSS in the vicinity, comparing the CN0 of the same satellite from different frequency bands (i.e., only one band is interfered with), or comparing the CN0 from different GNSSs (e.g., an interference impacts GPS L1 C/A differently than GLONASS G1). Evaluating the CN0 is a simple post-processing method and can be applied independently of the GNSS receiver hardware. Therefore, it is a popular method to detect interference signals with Android smartphones [40; 41; 42; 43], automatic dependent surveillance-broadcast (ADS-B) flight data [44], or publicly available data [45]. Furthermore, the SSC assumes that the receiver is not in saturation and it is the theoretical degradation of an ideal receiver. If the receiver is saturated, or non-linear effects from the analog frontend are present, the CN0 degrades much faster [39], and ultimately disrupts tracking altogether. Processing and analyzing the raw received signal is another approach for interference detection. Generally, the signal is processed to obtain some metric to detect [46]:  
\[m(x[n]) \begin{array}{c}\stackrel{{\mathcal{H}_{1}}}{{\underset {\mathcal{H}_{0}}{\leq}}}\lambda\\ \stackrel{{\mathcal{H}_{1}}}{{\underset{\mathcal{H}_{0}}{\leq}} }\lambda\end{array}