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Neural Augmented Kalman Filters for Road Network assisted GNSS positioning

arXiv 2507.00654 ↗First posted 1 Jul 2025cs.LG cs.SY eess.SPcited by 0

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Abstract (arXiv)

The Global Navigation Satellite System (GNSS) provides critical positioning information globally, but its accuracy in dense urban environments is often compromised by multipath and non-line-of-sight errors. Road network data can be used to reduce the impact of these errors and enhance the accuracy of a positioning system. Previous works employing road network data are either limited to offline applications, or rely on Kalman Filter (KF) heuristics with little flexibility and robustness. We instead propose training a Temporal Graph Neural Network (TGNN) to integrate road network information into a KF. The TGNN is designed to predict the correct road segment and its associated uncertainty to be used in the measurement update step of the KF. We validate our approach with real-world GNSS data and open-source road networks, observing a 29% decrease in positioning error for challenging scenarios compared to a GNSS-only KF. To the best of our knowledge, ours is the first deep learning-based approach jointly employing road network data and GNSS measurements to determine the user position on Earth.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 44 references that are in the map. First posted on arXiv 1 Jul 2025.

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10 of 44 references.

References · 10

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
  4. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
  5. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
  6. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
  7. 2021KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamicscited by 132
  8. 2021Improving GNSS Positioning using Neural Network-based Correctionscited by 10
  9. 2024AI-Aided Kalman Filterscited by 3
  10. 2024GNSS Positioning using Cost Function Regulated Multilateration and Graph Neural Networkscited by 1

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