利用地图辅助 GNSS 数据协调的有轨电车定位
Tram Positioning with Map-Enabled GNSS Data Reconciliation
AI总结：
针对城市多径环境下的有轨电车定位问题，提出结合轨道地图与 IEKF 的线性复杂度 GNSS 观测量协调方法，并通过仿真和真实数据验证其相对基线的性能提升。
摘要：
本文提出一种解决有轨电车定位问题的方法，通过对全球导航卫星系统（GNSS）观测量和轨道地图进行定制化处理来实现。该方法的动因是密集城市环境中的次优性能：在这些环境中，通往 GNSS 卫星的直接视线常被遮挡，从而导致 GNSS 信号的多径传播。所提出的概念基于迭代扩展卡尔曼滤波（IEKF），并且相对于 GNSS 测量数量具有线性复杂度，这与其他一些缓解多径信号传播的技术不同。该技术在仿真示例和真实数据上均进行了演示。相对于仿真真实位置所得的均方根误差表明，与基线定位方法相比，所提出的解决方案能够提升性能。在真实数据实验中也取得了类似结果；实验将到有轨电车轨道的正交投影视为真实位置，而该位置在现实场景中并不可用。这一概念验证所展示的结果，未来还可通过实施模型库方法或基于 χ² 的异常 GNSS 伪距测量拒绝机制进一步改进。
英文摘要：
This paper presents an approach to tackle the problem of tram localization through utilizing a custom processing of Global Navigation Satellite System (GNSS) observables and the track map. The method is motivated by suboptimal performance in dense urban environments where the direct line of sight to GNSS satellites is often obscured which leads to multipath propagation of GNSS signals. The presented concept is based upon the iterated extended Kalman filter (IEKF) and has linear complexity (with respect to the number of GNSS measurements) as opposed to some other techniques mitigating the multipath signal propagation. The technique is demonstrated both on a simulated example and real data. The root-mean-squared errors from the simulated ground truth positions show that the presented solution is able to improve performance compared to a baseline localization approach. Similar result is achieved for the experiment with real data, while treating orthogonal projections onto the tram track as the true position, which is unavailable in the realistic scenario. This proof-of-concept shows results which may be further improved with implementation of a bank-of-models method or $χ^2$-based rejection of outlying GNSS pseudorange measurements.
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