# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 6 Test Setup
### Results--L.I.N.K.--Lane Detection  
Results of experiment Exp-C extend experiment Exp-B and showcase the challenges of reliable detection and classification in dynamic scenarios. Figure 20 shows the setup of the lane-detection experiment. Figure 19: Generalization of model by removing the class “Signal 4” in the training data. Figure 18: Confusion matrix of RF train on distances between sensor and interference signal. This scenario mimics a typical motorway in Germany with two lanes going in the same direction. The lane detection experiment consists of four sensor nodes. Two are on the left (blue and purple sensors) of the highway, and two are on the right (yellow and green). They are symbolically mounted on each side of a bridge crossing. The \"Front\" and \"Back\" zones represent the entrance and exit zones of a typical tunnel of a highway bridge. In the \"Front\" zone, two sensor nodes are placed at the purple (left) and green (right) markers. In the \"Back\" zone, two sensor nodes are placed at the blue (left) and yellow (right) markers. The two walls reflecting inside and absorbing outside represent the reflection properties of the bridge. The top row of photos in Figure 20 shows the trajectory (white arrow) of a vehicle with no interference along Lane A (left lane). The bottom row shows the trajectory (red arrow) of a vehicle carrying a PPD along Lane B (right lane). The sensor data streams (i.e., SDR vectors) received from the four sensor nodes are collected, synchronized, and used for lane classification. The data are pre-processed so that the sensor data are synchronized with the interference signal reference position. Hence, every position of the interference signal is known, and the corresponding signals are present at the four sensor stations at all times. From there, the sensor data are split based on the interference positions associated with a given lane. Thus, a dataset that contains two classes, Lane 1 and Lane 2, is created. From there, a classification method is formalized to determine the trajectory of the PPD. Additionally, it is possible to regress the absolute position in XY-coordinates on the dataset to determine the exact position of the PPD [43]. This experiment investigates how many (1 to 4) sensors are necessary to reliably classify the lane where an interference signal is driving. Table 3 shows the results of lane detection. The results show that lane detection is improbable with a single sensor, but classification is possible. However, lane detection is achievable with two sensors (93.4%) and the classification is improved. The results indicate that adding more than three sensors adds too much (multipath) information that affects the localization performance. Two sensors \begin{table}
\begin{tabular}{l l l l} \hline \hline
**No. of Sensors** & **Interference Types** & \(\mathbf{F_{\beta=2}}\) **[\%]** & **Var. [\%]** \\ \hline
1 (purple) & & 76.3 & 7.5 \\
2 (front) & & 93.4 & 7.3 \\
2 (back) & 7 interference types & 91.7 & 6.8 \\
2 (purple, yellow) & with a total of 33 subclasses: Chirp, & 87.1 & 4.8 \\
2 (purple, blue) & Noise, Multitone,... & 85.8 & 5.7 \\
3 (2 front, blue) & & 81.9 & 7.2 \\
4 (2 front, 2 back) & & 82.5 & 6.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of our lane detection experiment. The best value is shown in green and bold. Figure 20: Lane detection experiment with four sensor nodes mounted on each side of a bridge crossing (two walls reflecting inside and absorbing outside) on a highway. (i.e., purple and green or blue and yellow, see Figure 20) on one side of the tunnel performed best. Instead, information from diagonal or three or more sensors reduces the lane detection accuracy. As fingerprint-based localization (detection) exploits the additional multipath information in the signal to map a unique fingerprint of the signal to a specific position, it is possible that adding too much information may not add additional information, but may result in either redundant information or even mirrors the information.