Positioning, navigation, and timing (PNT) is essential for Internet of Things (IoT) communications and location-based services. Although global navigation satellite system (GNSS) can provide accurate PNT in open areas, obtaining reliable PNT is still a considerable technical challenge in complex urban environments. This is because the GNSS signals are more likely to be affected by multipath interference and nonline of sight (NLOS) reception issues arising from the obstructions and reflections in built environments. These introduce range measurement errors that degrade the GNSS positioning accuracy. This article proposes two resilient pseudorange error prediction and correction strategies to improve the GNSS positioning accuracy in urban environments. In particular, considering the carrier-to-noise density ( $C/N$ textsubscript 0), satellite elevation angle, and local positional information, the random forest-based pseudorange error prediction and correction models are constructed in two variations, including: 1) the point-based correction (PBC) and 2) the grid-based correction (GBC). The final improved positioning solution is then calculated by using the least square method (LSM) of the corrected pseudoranges. Kinematic test results in urban environments show that both variations of the proposed model can improve the positioning accuracy by 42.9% and 40.8% in horizontal, and by 60.1% and 63.3% in 3-D, respectively, compared to the positioning results obtained by the traditional method without pseudorange error corrections. The improvements are 41.1% and 38.9% in horizontal, and 45.7% and 50.0% in 3-D, respectively, compared with traditional elevation angle weighting method.

中文翻译：

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城市地区 GNSS 定位的弹性伪距误差预测和校正

定位、导航和授时 (PNT) 对于物联网 (IoT) 通信和基于位置的服务至关重要。尽管全球导航卫星系统（GNSS）可以在开阔地区提供准确的 PNT，但在复杂的城市环境中获得可靠的 PNT 仍然是一个相当大的技术挑战。这是因为 GNSS 信号更容易受到建筑环境中的障碍物和反射引起的多径干扰和非视距 (NLOS) 接收问题的影响。这些引入了降低 GNSS 定位精度的距离测量误差。本文提出了两种弹性伪距误差预测和校正策略，以提高城市环境中的 GNSS 定位精度。特别是，考虑到载噪比密度（ $C/N$textsubscript 0)、卫星仰角和本地位置信息，基于随机森林的伪距误差预测和校正模型以两种变体构建，包括：1) 基于点的校正 (PBC) 和 2) 基于网格的校正（GBC）。然后使用校正后的伪距的最小二乘法（LSM）计算最终改进的定位解。城市环境中的运动学测试结果表明，所提出模型的两种变体在水平方向上的定位精度分别提高了 42.9% 和 40.8%，在 3-D 中分别提高了 60.1% 和 63.3%。没有伪距误差校正的传统方法。水平方向的改进分别为 41.1% 和 38.9%，3-D 方向的改进分别为 45.7% 和 50.0%，