Few-Sample GNSS Signal LOS/NLOS Identification via Physical-Semantic Distillation From Large Language Models

Global Navigation Satellite Systems (GNSS) are widely used for positioning and navigation, yet NLOS signals severely degrade performance in urban environments. Existing deep learning-based LOS/NLOS identification methods require large labeled datasets and lack physical interpretability. To address this issue, we propose a few-sample GNSS signal LOS/NLOS identification framework based on physical-semantic distillation from large language models (LLMs), where physics-informed reasoning generates soft NLOS labels to guide a lightweight student model. Experiments show a 4.5% accuracy improvement on a public dataset with 1% training data, and 2.4% gain on a self-collected dataset under the same setting.