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）在交通系统中为车辆提供精确且一致的定位服务，发挥着至关重要的作用。然而，在城市峡谷等复杂环境中，多径效应和非视距（NLOS）接收会导致GNSS观测数据失真。在此类情况下，传统用于分类和剔除异常GNSS观测数据的方法可能失效，进而导致状态估计不可靠及系统运行存在安全隐患。本文提出一种基于深度学习的方法，通过将GNSS观测数据分析视为时空建模问题，实现NLOS接收检测与GNSS伪距误差预测。与先前研究相比，我们构建了类Transformer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，我们使用了来自香港和亚琛市的标注数据集，并引入一种利用激光雷达地图标注GNSS观测数据的生成流程。在实验研究中，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，进一步开展网络组件的消融实验，并将NLOS检测与数据分布外场景整合至状态估计器中。结果表明，相较于其他模型，本网络在精确率和召回率上均有提升。此外，通过分类与剔除NLOS观测数据，所提方法在实际车辆定位中有效避免了轨迹发散现象。