# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 6 Test Setup  
Figure 15 shows some pictures of the measurement campaign. In the original conference paper, several results in an industrial measurement hall were conducted [36]. However, the results in this article are extended to an anechoic chamber to evaluate PPDs under realistic electromagnetic (EM) propagation through a vehicle. Therefore, this test case represents the application to detect and classify vehicular mounted interference commonly used for automatic toll collection (ATC). This case is challenging, as the PPD interferes significantly with the onboard navigation system, but the metal vehicle naturally shields the signal from an external observer. Furthermore, the vehicle results in different scattering profiles from different directions, which distorts the signal making classification more challenging.  
In contrast to previous studies [62; 66; 67] that employed ML and DL methods for interference analysis, the framework is not only tested on synthetic data from laboratory simulations, but also in a deterministic real-world environment. Several experiments explore the limitations of the framework and the challenge of reliable detection and classification. Four common PPDs are chosen, each transmitting a chirp signal with a bandwidth ranging from 10 to 30 MHz.  
The following experiments are performed:  
* Exp-A: Static setup with one of the PPDs inside a driver cab of a van and a fixed distance of 6 m between the car and the sensor. The interference signals were activated sequentially. Only one at a time was transmitting.
* Exp-B: Static setup with one of the PPDs was placed inside the van at the driver's side while the sensor was placed outside at distances of 3, 6, or 9 m from the vehicle. The experiment was conducted sequentially with all four interference signals, with only one interference signal transmitting at a time.
* Exp-C: To test complex real-world conditions and dynamic scenarios with moving interference, including line-of-sight (LOS), severe multipath, and non-line-of-sight (NLOS) conditions. This experiment was conducted at the L.I.N.K Halle Test Center at Fraunhofer Nurnberg. A person moved a COTS PPD across a tunnel of reflector wall, miming a typical motorway bridge in Germany. Four sensor nodes were mounted down each side of the bridge crossing.  
Each experiment included no interference, and the four mentioned interference signals facilitate detection and classification methods. During experiments Exp-A and Exp-B, which ran for more than 5 h, seven sensors collected more than 125,000 samples at 1 Hz. Figure 16 shows the distribution of the recorded data. The data set is divided into 70% training data and 30% validation or test data for each benchmark.  
The original conference paper that this article extends [36], considered power scaling to shrink the effective detection distance down and to facilitate simple testing in a measurement hall. It indicates an approximate increase of factor 30 in the detection and classification range to what is tested. This approach is also used in Exp-C and demonstrates that detection and classification ranges for an equivalent PPD of 110 m is possible.  
effectively differentiate between the four types of interference signal signals and the clean signal with an accuracy exceeding 94%.  
RF vs. ResNet  
The 64 SDR features with 5 s intervals are plotted as spectrograms and saved as PNG files (as shown in Figure 12) to compare the performance and accuracy of the RF and ResNet models. These files are then utilized for training a ResNet18 model, a deep CNN architecture with 18 layers (see Figure 11). The same 64 SDR features are used for an RF model with 1000 DTs (see Figure 13) to compare the accuracy. The overall accuracy of the ResNet18 model is marginally higher by approximately 1% compared to the RF model (see Figure 17). However, the ResNet18 model trains more than 100 times longer and requires more than 270 times longer for an inference step of a single sample (see Table 2).