The global navigation satellite systems (GNSS) play a vital role in transport systems for accurate and consistent vehicle localization. However, GNSS observations can be distorted due to multipath effects and non-line-of-sight (NLOS) receptions in challenging environments such as urban canyons. In such cases, traditional methods to classify and exclude faulty GNSS observations may fail, leading to unreliable state estimation and unsafe system operations. This work proposes a deep-learning-based method to detect NLOS receptions and predict GNSS pseudorange errors by analyzing GNSS observations as a spatio-temporal modeling problem. Compared to previous works, we construct a transformer-like attention mechanism to enhance the long short-term memory (LSTM) networks, improving model performance and generalization. For the training and evaluation of the proposed network, we used labeled datasets from the cities of Hong Kong and Aachen. We also introduce a dataset generation process to label the GNSS observations using lidar maps. In experimental studies, we compare the proposed network with a deep-learning-based model and classical machine-learning models. Furthermore, we conduct ablation studies of our network components and integrate the NLOS detection with data out-of-distribution in a state estimator. As a result, our network presents improved precision and recall ratios compared to other models. Additionally, we show that the proposed method avoids trajectory divergence in real-world vehicle localization by classifying and excluding NLOS observations.

翻译：全球导航卫星系统(GNSS)在交通系统中对实现精确且一致的车辆定位至关重要。然而，在城市峡谷等复杂环境中，多路径效应和非视距(NLOS)接收会扭曲GNSS观测量。在此类场景下，传统分类与排除故障GNSS观测量的方法可能失效，导致状态估计不可靠及系统运行不安全。本研究提出一种基于深度学习的方法，通过将GNSS观测量建模为时空问题进行NLOS检测与伪距误差预测。与先前工作相比，我们构建了类似Transformer的注意力机制以增强长短期记忆(LSTM)网络，从而提升模型性能与泛化能力。为训练和评估所提网络，我们采用了香港与亚琛两座城市的标注数据集，并引入利用激光雷达地图标注GNSS观测量的数据集生成流程。实验研究中，我们将所提网络与深度学习模型及经典机器学习模型进行对比，同时开展网络组件的消融实验，并将NLOS检测与数据分布外检测集成至状态估计器。结果表明，相较于其他模型，我们的网络在精确率与召回率上均有提升。此外，通过分类与排除NLOS观测量，该方法可有效避免真实车辆定位中的轨迹发散问题。