简单机器学习模型在GNSS干扰检测中表现优于复杂深度学习模型。
Towards Simple Machine Learning Baselines for GNSS RFI Detection
- 用轻量级机器学习模型作为基线，对比复杂深度模型。
- 基线模型达到91%准确率，超越主流深度学习方法。
- 适合关注实用性和可解释性的研究人员参考。
GNSS射频干扰（RFI）检测领域的机器学习研究常缺乏对深度学习架构选择的实证依据。本文主张研究方向应从构建日益复杂的深度模型转向评估其与可解释、轻量级机器学习基线的真实性能对比。基于瑞士空军与瑞士空中救援组织（Rega）提供的大规模数据集，并由瑞士空管服务公司（Skyguide）预处理，我们发现：当前最先进的深度学习模型在GNSS RFI检测任务中频繁落后于精心设计的简单机器学习方法。一个基础模型实现91%的检测准确率，优于更复杂的深度学习对手。结果凸显了务实解决方案的有效性，为该关键应用领域未来研究提供了重要启示。
原文摘要 · Abstract (English)
Machine learning research in GNSS radio frequency interference (RFI) detection often lacks a clear empirical justification for the choice of deep learning architectures over simpler machine learning approaches. In this work, we argue for a change in research direction-from developing ever more complex deep learning models to carefully assessing their real-world effectiveness in comparison to interpretable and lightweight machine learning baselines. Our findings reveal that state-of-the-art deep learning models frequently fail to outperform simple, well-engineered machine learning methods in the context of GNSS RFI detection. Leveraging a unique large-scale dataset collected by the Swiss Air Force and Swiss Air-Rescue (Rega), and preprocessed by Swiss Air Navigation Services Ltd. (Skyguide), we demonstrate that a simple baseline model achieves 91\% accuracy in detecting GNSS RFI, outperforming more complex deep learning counterparts. These results highlight the effectiveness of pragmatic solutions and offer valuable insights to guide future research in this critical application domain.
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