# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 3 Cases for Investigation
### Jamming Detection  
Several different techniques may be employed for detection of the interference in a GNSS receiver. These are often separated into pre-correlation and post-correlation techniques. Frequency domain detection using the fast Fourier transform (FFT) is a pre-correlation method used by [26; 27; 28], who all used statistical inference methods in order to detect RFI in a signal. Another pre-correlation approach uses the adaptive gain control (AGC) value output by the receiver's analogue-to-digital converter (ADC). This approach was adopted by [29; 30], and shown to accurately detect RFI. Post-correlation techniques are often based on the receiver's own determination of the carrier-to-noise ratio (C/N\({}_{0}\)). As this is a measure of the ratio between GNSS signal strength and the noise floor, it will naturally drop when noise increases. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **GPS** & **GLONASS** & **Galileo** & **BeiDou** \\ \hline \multirow{2}{*}{Bands} & L1 C/A, L2P, & G1 C/A, G2C, & E1, E5a, E5b, & \\  & L2C, L5 & G3 & E5 AltBOC & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Global navigation satellite system (GNSS) signals tracked by reference stations in the Testbed in Aarhus for Precision Positioning and Autonomous Systems (TAPAS) network. Several authors have used this measure to detect RFI, and methods range from using the statistical properties of the C/N\({}_{0}\) measurement [31] and evaluating the change in C/N\({}_{0}\) (\(\Delta\)C/N\({}_{0}\)) [32; 33], to examining the repeatability of C/N\({}_{0}\) in static receivers [34]. A final post-correlation approach is the divergence-based RFI monitor, as described in [35], basically by estimating current error levels. Each approach comes with its own set of advantages. Evaluations of field experiments with different kinds of detectors were performed in both [19; 36], while [37] contains a comprehensive list of existing GNSS interference monitoring systems. For this work, we chose to implement a \(\Delta\)C/N\({}_{0}\) detector, specifically the sum-of-squares (SoS) detector first developed by [33]. The reason for this is that it requires only C/N\({}_{0}\) data, which are available on all GNSS receivers, which makes it a very general detector. For TAPAS, this allows us to search archived RINEX data for jamming in all stations through the complete operational period of the network. C/N\({}_{0}\) detectors have several known downsides. Primarily, C/N\({}_{0}\) ratio values are highly affected by the environment, and moving GNSS receivers would see frequent C/N\({}_{0}\) drops owing to trees, buildings, or other things blocking the satellite-receiver line-of-sight (LoS) [19; 36]. As all receivers in this case are permanent reference stations, i.e., static receivers in good, open sky conditions, using a C/N\({}_{0}\) detector should not pose such problems. C/N\({}_{0}\) detectors may also be affected by other conditions such as satellite signal loss or constellation adjustments/repositioning. For this paper, several extra checks were applied to the algorithm in order to avoid such false positives. They are, fortunately, not a big issue, as long as the real jamming incidents are also caught by the detector. In short, the SoS algorithm looks for correlated/simultaneous changes in C/N\({}_{0}\) values. In general, the algorithm can be described by the following equation:  
\[SoS=\sum_{k=0}^{N-1}\left[\sum_{i=0}^{I-1}\left(\frac{C_{i}}{N_{0}}[n-k]-\frac {C_{i}}{N_{0}}[n-k-1]\right)\right]^{2}\]  
where  
* n is the current epoch/sample;
* N is the number of epochs/samples that are checked by the algorithm;
* i denotes the respective C/N\({}_{0}\) tracking channel;
* I is the total number of satellites used for calculation of the SoS value. As jammers ordinarily target the L1/E1 band [4; 8; 14], detection in this investigation is done solely from the three most powerful GPS L1 signals received. This saves a lot of computations and minimizes noise from low elevation satellites, among others.