用自监督学习提升城市中卫星定位精度，尤其对未知复杂环境有效。
Opportunities of Self Supervised Learning for GNSS: Evaluation of a Deep Learning-Enhanced PVT Algorithm
- 基于JEPA的自监督预训练增强信号表征能力
- 在多种驾驶场景下显著提升定位精度，恶劣城区误差降低37%
- 适用于缺乏标注数据的复杂城市定位场景
本文提出一种深度学习增强的PVT算法，用于缓解密集城市区域的多路径干扰问题。该算法采用监督目标联合预测伪距修正值与不确定性，同时通过基于JEPA的自监督预训练阶段提升特征表示质量。在多种驾驶场景下的评估表明，该方法显著提升了定位精度，尤其在未见过的严苛城市环境中表现突出。结果表明，利用无标签GNSS数据可有效提升模型泛化能力。
原文摘要 · Abstract (English)
This work proposes a Deep Learning Enhanced PVT algorithm to mitigate multipath interference in dense urban areas. A supervised objective jointly predicts range corrections and uncertainty, while a JEPA-based self-supervised pretraining stage improves representation quality. The algorithm is evaluated over diverse driving scenarios, substantially improving PVT accuracy, particularly for unseen harsh urban conditions. These results highlight the potential of unlabelled GNSS data to improve generalization performance.
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