AI 中文总结
本研究针对资源受限平台，提出结合自适应陷波滤波器特征与梯度提升决策树的轻量算法，实现实时预相关GNSS干扰分类，在多类RFI事件上达到高准确率并验证了资源利用率。
AI 中文摘要
射频干扰（RFI）仍是安全关键应用中GNSS的重大威胁。由于单一抑制方法无法对所有干扰源有效，需可靠分类以在运行中选择合适对策。为防止失锁，RFI分类需实时运行，通常在资源受限的嵌入式平台上，因此需要轻量算法。现有研究采用简单的基于规则的算法检测和表征某些类型干扰源，但该方法无法扩展到所有可能的RFI技术，数据驱动的学习算法更合适。为满足这些约束，本研究考虑预相关RFI分类，重点是仍能提供高准确率的紧凑算法。我们首先引入一组新的轻量输入特征，这些特征来自虚拟自适应陷波滤波器（ANF）的瞬时频率预测。我们观察到，将这些新特征与现有文献中的其他频谱特征结合，可提高对窄带和宽带非平稳RFI的分类准确率。接下来，我们对紧凑学习分类器（如梯度提升决策树）进行基准测试，以在严格的计算和内存预算下实现准确预测。评估涵盖大量模拟和记录的RFI事件，包括近期研究中的公开数据集。最后，我们在紧凑的CRPA平台（EDGE Microwave HEDGE8008）上测量我们的模型和现有文献中代表性方法的资源利用率。
英文摘要
Radio-frequency interference (RFI) remains a significant threat to GNSS in safety-critical applications. Since no single mitigation method is effective for all interferers, reliable classification is needed to select appropriate countermeasures during operation. To prevent loss of lock, RFI classification must run in real time, typically on resource-constrained embedded platforms, necessitating lightweight algorithms. While prior works realize this with simple rule-based algorithms for detecting and characterizing certain types of interferers, this approach does not scale to the broad space of all possible RFI techniques, and data-driven learned algorithms are a better fit. To satisfy these constraints, this work considers pre-correlation RFI classification with an emphasis on compact algorithms that still provide high accuracy. We first introduce a new set of lightweight input features derived from the instantaneous frequency predictions of a virtual adaptive notch filter (ANF). We observe improved classification accuracy for both narrowband and broadband non-stationary RFI by combining these new features with other spectral features from prior literature. Next, we benchmark compact learned classifiers such as gradient-boosted decision trees for accurate prediction under tight compute and memory budgets. The evaluation spans a broad set of simulated and recorded RFI events, including publicly available datasets from recent studies. Finally, we measure resource utilization for our models and for representative methods from the literature, on a compact CRPA platform (EDGE Microwave HEDGE8008).
CommentsPaper presented at ION ITM 2026, Los Angeles, CA. Author-submitted version. Find the camera-ready version at the ION repository
