<ul><li>Machine learning research in GNSS radio frequency interference (RFI) detection often lacks justification for decisions made in deep learning-based model architectures.</li><li>This paper challenges the status quo in machine learning approaches for GNSS RFI detection and advocates for a shift in focus to simpler and more interpretable machine learning baselines.</li><li>The findings suggest the need for the development of simple and interpretable machine learning methods and demonstrate the effectiveness of a simple baseline for GNSS RFI detection.</li><li>The results show that the simple baseline outperforms complex deep learning architectures with 91% accuracy in detecting potential GNSS RFI.</li></ul>

Towards Simple Machine Learning Baselines for GNSS RFI Detection

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