Evaluating ML Robustness in GNSS Interference Classification, Characterization \& Localization
<|reference_start|>Evaluating ML Robustness in GNSS Interference Classification, Characterization \& Localization: Jamming devices present a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. A critical preliminary measure involves the reliable classification of interferences and characterization and localization of jamming devices. This paper introduces an extensive dataset compromising snapshots obtained from a low-frequency antenna, capturing diverse generated interferences within a large-scale environment including controlled multipath effects. Our objective is to assess the resilience of ML models against environmental changes, such as multipath effects, variations in interference attributes, such as the interference class, bandwidth, and signal-to-noise ratio, the accuracy jamming device localization, and the constraints imposed by snapshot input lengths. By analyzing the aleatoric and epistemic uncertainties, we demonstrate the adaptness of our model in generalizing across diverse facets, thus establishing its suitability for real-world applications. https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/controlled_low_frequency<|reference_end|>
@article{heublein2024evaluating,
    title={Evaluating ML Robustness in GNSS Interference Classification,
  Characterization \& Localization},
    author={Lucas Heublein and Tobias Feigl and Thorsten Nowak and Alexander
  R"ugamer and Christopher Mutschler and Felix Ott},
    journal={arXiv preprint arXiv:2409.15114},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15114},
    primaryClass={cs.AI}
}