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.

翻译：大型语言模型（LLMs）是应用于自然语言处理、信息检索和推荐系统等多个领域的先进人工智能系统。尽管LLMs具有适应性强和效率高的特点，但在信号处理任务，尤其是全球导航卫星系统（GNSS）干扰监测领域，尚未得到广泛探索。GNSS干扰监测对于确保道路车辆定位的可靠性至关重要，这是众多应用的关键需求。然而，基于GNSS的定位易受干扰设备的影响，从而可能损害其精度。主要目标是识别、分类并减轻这些干扰。由于固有的复杂性，包括多径效应、多样的干扰类型、变化的传感器特性和卫星星座，解释GNSS快照及相关干扰面临重大挑战。本文从一个大型GNSS数据集中提取特征，并利用LLaVA从广泛的知识库中检索相关信息。我们采用提示工程来解释干扰和环境因素，并利用t-SNE分析特征嵌入。我们的研究结果表明，所提出的方法能够在GNSS上下文中进行视觉和逻辑推理。此外，我们的流程在干扰分类任务中优于最先进的机器学习模型。