# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 5 Algorithmic Design
### Processing
#### 5.3.1 Detection vs. Classification  
The detection problem is formulated as a binary classification problem. Hence, the supervised detection of an anomaly in a set of features employs the same architectures that are employed for the supervised classification task. Consequently, Section 5.3.2 describes the architectures of the AI models that are employed for both detection and classification tasks. However, before introducing the architectures, the detection and classification tasks are presented.  
The first stage of interference monitoring is to detect a interference signal. There are several methods to detect interference, and they are grouped into two categories. First, GNSS receiver output monitoring considers the interference impact on GNSS processing. Second, signal spectrum monitoring considers the raw received signal samples. Therefore,  
Figure 11: Overview of the ResNet18 architecture. Note that data flows from left to right and from top to bottom. The colored boxes represent the following components: blue = _Convolutional layer_, red = _ReLU layer_, green = _BatchNorm Normalization_ or _Max Pooling layer_, and white = _Input/Output layer_.  
Figure 12: Spectrograms of 5 s of signals, yellow is high power and blue is low power. The signals of the interference signals are 10 to 30 MHz wide, but the sensor node records only a bandwidth of 2.56 MHz so it can see a small portion of the signal. The signals are labeled as Interference signal type 1 to type 4 (**a–d**), and None (signal without interference signals) (**e**).  
a great approach is to leverage both strategies for improved performance. Consequently, supervised learning techniques are used to exploit the most relevant information from features to optimize the process of interference detection and classification.  
Traditional classification has several limitations: the effort of deriving and tuning optimal detectors, the inability to adapt to new waveforms without new development, high processing costs, and complex logic trees. CNNss are a good approach as they consider the signal spectrogram as an image [62], which facilitates image classification approaches from the computer science literature.  
The categorization process is developed as a supervised multi-class classification task that returns the interference type and the uncertainty of the prediction, i.e., mean and variance. A RF, proposed by [PERSON] et al. [99], is deployed for this task. It is a classifier containing multiple DTs, and voting returns the final result. The RF developed and optimized in the presented pipeline efficiently provides the most accurate classification results and the corresponding uncertainty.  
The pessimistic metric \(F_{\beta=2}\)[100] was used to evaluate the results. This metric weights recall and precision, which measures the errors related to the positive class, to give false-negative estimates more importance, with \(\beta\) being the relative importance of recall over precision. The maximum score is 1.0, which indicates an accurate classification.  
\[F_{\beta=2}=\frac{\big{(}1+\beta^{2}\big{)}\mathrm{TP}}{(1+\beta^{2})\mathrm{ TP}+\beta^{2}\mathrm{FN}+\mathrm{FP}}=\frac{5\mathrm{TP}}{5\mathrm{TP}+4 \mathrm{FN}+\mathrm{FP}} \tag{8}\]  
where TP is the true positive, FN is the false negative, and FP is the false positive. This metric penalizes missed outlier detections (i.e., FN), which is crucial for interference detection.