Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization
This work addresses the threat of GNSS jamming by assessing ML robustness for real-world applications, but it is incremental as it focuses on evaluation rather than introducing new methods.
The paper tackled the problem of evaluating machine learning models for classifying, characterizing, and localizing GNSS interference, using a new dataset with controlled multipath effects, and found that analyzing uncertainties helps models generalize across environmental changes and interference attributes.
Jamming devices disrupt signals from the global navigation satellite system (GNSS) and pose a significant threat, as they compromise the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. A critical preliminary countermeasure involves the reliable classification of interferences and the characterization and localization of jamming devices. This paper introduces an extensive dataset comprising snapshots obtained from a low-frequency antenna that capture various generated interferences within a large-scale environment, including controlled multipath effects. Our objective is to assess the resilience of machine learning (ML) models against environmental changes, such as multipath effects, variations in interference attributes, such as interference class, bandwidth, and signal power, the accuracy of jamming device localization, and the constraints imposed by snapshot input lengths. Furthermore, we evaluate the performance of a diverse set of 129 distinct vision encoder models across all tasks. By analyzing the aleatoric and epistemic uncertainties, we demonstrate the adaptability of our model in generalizing across diverse facets, thus establishing its suitability for real-world applications. Dataset: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/controlled_low_frequency