# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 2 Background to Interference Monitoring
### Classification  
Interference classification (often called characterization in the GNSS literature [16; 19; 21]) identifies the interference waveform. If the waveform type is known, the purpose of the signal can be determined, which assists in finding and dealing with the interference signal. Figure 2 shows some examples of different interference signals that can be classified. Classification is a significantly more complex problem than detection. The traditional approach is to create several purposefully built detectors (i.e., several metrics based on Equation (3)) for each class of signal considered and maximize the correct decision thresholds to distinguish between them [17]. For example, it can be done by using a multiple-hypothesis generalized likelihood ratio test (GLRT). However, it requires significant resources and could be more practical. Hence, a detector tree structure is often deployed to simplify classification. Pre-processing the signal often supports the classification engines. Typical inputs for classification are the spectrogram of the received signal, amplitude statistics, and the signal PSD. 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. The latest state-of-the-art techniques employ ML or deep learning (DL) methods to compensate for these weaknesses and to reliably and accurately classify interference signals [36; 43; 57; 58]. Modern ML- and DL-based methods are on par with or even superior to classical techniques in practical situations, as these methods learn non-deterministic and non-linear correlations from data. It improves the classification performance and robustness even in multipath and dynamic cases. In contrast to classical model-driven classification techniques, supervised data-driven approaches implicitly learn these thresholds or informative patterns, enabling accurate classification even near the noise floor [59]. In addition, methods based on artificial intelligence (AI) use exogenous information such as multipath components or other interference sources to map even more specific patterns and specific classes [60]. AI-based methods also implicitly denoise to enable accurate and robust classification [61]. Figure 2: Classification example of different waveform types, showing the spectrograms. Yellow is high power and blue is low power, the signals are: (**a**) Single-tone, (**b**) Multitone, (**c**) Chirp, and (**d**) Pulsed noise. Recent research applied these techniques by adapting support vector machines (SVMs), convolutional neural networks (CNNs) [62], or decision trees (DTs) [63] to the challenges of the GNSS community. The effectiveness of these methods on real data has yet to be determined. Only [PERSON] et al. [64] compared ResNet18 and time-series (TS) Transformer architectures regarding real-world data classification performance. However, signal properties in the time and frequency domains are similar in the presence and absence of interference signals. Therefore, individually utilizing these methods results in information loss during optimization and negatively impacts the classification accuracy. The performance of ResNet18 and TS-Transformer on data that more accurately reflect the complexities of real-world scenarios, including multipath effects, variable distances and power levels, motion dynamics, and real-world noise, remains to be determined. Based on the current literature, CNNs are a viable approach as they treat the spectrogram of a signal as an image [57; 62; 65], which facilitates image classification approaches from the computer science literature. Statistical methods incorporating a more diverse range of pre-processed features may benefit using SVMs [66; 67]. Recently, autoencoders (AEs) have also proven useful [58; 68]. ML has a significant advantage in learning critical information and automatically optimizing the classification process for optimal performance.