Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network
Haoming Zhang (RWTH Aachen University)
Zhanxin Wang (RWTH Aachen University)
Heike Vallery (RWTH Aachen University, TU Delft - Biomechatronics & Human-Machine Control)
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Abstract
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.