Large language models (LLMs) are advanced AI systems applied across various domains, including NLP, information retrieval, and recommendation systems. Despite their adaptability and efficiency, LLMs have not been extensively explored for signal processing tasks, particularly in the domain of global navigation satellite system (GNSS) interference monitoring. GNSS interference monitoring is essential to ensure the reliability of vehicle localization on roads, a critical requirement for numerous applications. However, GNSS-based positioning is vulnerable to interference from jamming devices, which can compromise its accuracy. The primary objective is to identify, classify, and mitigate these interferences. Interpreting GNSS snapshots and the associated interferences presents significant challenges due to the inherent complexity, including multipath effects, diverse interference types, varying sensor characteristics, and satellite constellations. In this paper, we extract features from a large GNSS dataset and employ LLaVA to retrieve relevant information from an extensive knowledge base. We employ prompt engineering to interpret the interferences and environmental factors, and utilize t-SNE to analyze the feature embeddings. Our findings demonstrate that the proposed method is capable of visual and logical reasoning within the GNSS context. Furthermore, our pipeline outperforms state-of-the-art machine learning models in interference classification tasks.

本研究解决了大语言模型在全球导航卫星系统（GNSS）干扰监测中应用不足的问题。通过提取GNSS数据集的特征，并使用LLaVA从知识库中检索信息，论文提出了一种创新的提示工程方法，以解析干扰及环境因素。研究结果显示，该方法在干扰分类任务中优于现有的机器学习模型，具有良好的视觉和逻辑推理能力。

基于特征嵌入的多模态文本提示工程在大语言模型中的应用：全球导航卫星系统干扰特征化