  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.

Robotics;Artificial Intelligence