用伪标签方法让卫星干扰监测站实现跨环境高泛化分类
Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling
- 结合蒙特卡洛与深度集成的不确定性投票机制，减少95%以上标注数据需求
- 在真实道路环境中实现对干扰源的准确分类，适应性显著提升
- 适合需要少标注数据、跨场景部署的无线干扰监测系统开发者
全球导航卫星系统（GNSS）接收机的精度受干扰设备影响严重，因此检测干扰源至关重要。然而，由于真实环境中缺乏标注数据，基于机器学习（ML）的干扰分类面临挑战。本文提出一种面向高速公路沿线协同部署监测站的半监督方法，结合蒙特卡洛与深度集成的不确定性投票机制，在仅需不到5%标注样本的情况下，实现强泛化能力，并显著提升模型在不同环境间的适应性。实验表明，该方法在从室内环境迁移到真实场景时仍保持优异性能。
原文摘要 · Abstract (English)
The accuracy of global navigation satellite system (GNSS) receivers is significantly compromised by interference from jamming devices. Consequently, the detection of these jammers are crucial to mitigating such interference signals. However, robust classification of interference using machine learning (ML) models is challenging due to the lack of labeled data in real-world environments. In this paper, we propose an ML approach that achieves high generalization in classifying interference through orchestrated monitoring stations deployed along highways. We present a semi-supervised approach coupled with an uncertainty-based voting mechanism by combining Monte Carlo and Deep Ensembles that effectively minimizes the requirement for labeled training samples to less than 5% of the dataset while improving adaptability across varying environments. Our method demonstrates strong performance when adapted from indoor environments to real-world scenarios.
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