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This paper introduces a Deep Learning Enhanced Positioning, Velocity, and Time (PVT) algorithm designed to address multipath interference in dense urban environments. By employing a supervised objective to predict range corrections and uncertainty alongside a self-supervised pretraining stage using Joint Embedding Predictive Architecture (JEPA), the algorithm significantly enhances representation quality. Evaluation across various driving scenarios demonstrates a marked improvement in PVT accuracy, especially in challenging urban conditions, underscoring the utility of unlabelled GNSS data for better generalization.
Unlabelled GNSS data can dramatically enhance PVT accuracy in urban environments, achieving substantial improvements even in unseen harsh conditions.
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.