Session E1: Emerging Technologies for Alternative, Resilient, and Intelligent PNT Systems

Machine Learning Model Uncertainty in GNSS Positioning
Paul Dobre, Shichuang Nie, Hongzhou Yang, Department of Geomatics Engineering, University of Calgary
Location: Peale (First Floor)
Date/Time: Wednesday, Sep. 10, 10:40 a.m.

Peer Reviewed

GNSS positioning methods such as Kalman filters, factor graph optimization, and weighted least squares (WLS) have recently been complemented by machine learning (ML) models aimed at improving positioning accuracy and robustness. ML has been applied to GNSS for signal classification, anomaly detection, environmental inference, and position correction. However, in challenging conditions—such as urban canyons or unfamiliar scenarios—ML models face epistemic (model) and aleatoric (data) uncertainty, which can result in overconfident yet incorrect predictions that compromise system integrity. This work proposes an uncertainty-aware ML framework to enhance GNSS positioning error estimation by quantifying and incorporating both epistemic and aleatoric uncertainty into the model output. A spatial transformer is used to predict GNSS positioning error based on satellite-specific observation features. The model output includes both a positioning error estimate and its associated uncertainty, which enables more reliable integration with traditional GNSS solutions. Uncertainty is quantified through a model ensemble approach that aggregates predictions from multiple models to estimate uncertainty. The benefits of incorporating uncertainty include better anomaly handling, increased model interpretability, improved retraining strategies via active learning, and the ability to fall back to traditional methods in high-uncertainty situations. The preliminary experimental results demonstrate that incorporating uncertainty improves positioning interpretability and robustness compared to a standard ML-enhanced GNSS pipeline through effectively identifying out-of-distribution scenarios and high input noise and guiding system fallback decisions.