In urban environments, where line-of-sight signals from GNSS satellites are frequently blocked by high-rise objects, GNSS receivers are subject to large errors in measuring satellite ranges. Heuristic methods are commonly used to estimate these errors and reduce the impact of noisy measurements on localization accuracy. In our work, we replace these error estimation heuristics with a deep learning model based on Graph Neural Networks. Additionally, by analyzing the cost function of the multilateration process, we derive an optimal method to utilize the estimated errors. Our approach guarantees that the multilateration converges to the receiver's location as the error estimation accuracy increases. We evaluate our solution on a real-world dataset containing more than 100k GNSS epochs, collected from multiple cities with diverse characteristics. The empirical results show improvements from 40% to 80% in the horizontal localization error against recent deep learning baselines as well as classical localization approaches.

我们的研究替代了启发式方法，使用基于图神经网络的深度学习模型来估计大城市环境下卫星测距误差，并分析了多次叠加的最优方法，提高了水平定位误差的准确性。实证结果显示与最近的深度学习基线模型和经典定位方法相比，我们的解决方案在水平定位误差上有40%至80%的改善。

利用成本函数调节的多基站测距和图神经网络的GNSS定位