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Volume 126
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10.3390/engproc2026126014
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Proceeding Paper

Toward an Interpretable Multipath Error Model from GNSS Observables Through the Application of Deep Learning †

by
Thomas Barbero
*,
Eustachio Roberto Matera
,
Bertrand Ekambi
,
Jeremy Chamard
and
Mathieu Ekambi
Abbia GNSS Technologies, 31100 Toulouse, France
*
Author to whom correspondence should be addressed.
†
Presented at the European Navigation Conference 2025 (ENC 2025), Wrocław, Poland, 21–23 May 2025.
Eng. Proc. 2026, 126(1), 14; https://doi.org/10.3390/engproc2026126014
Published: 14 February 2026
(This article belongs to the Proceedings of European Navigation Conference 2025)
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Abstract

Multipath degradation of GNSS measurements is the main source of error in urban areas. Robust mitigation of this error source is still a challenge for standalone low-cost GNSS receivers. The complexity associated with the development of Multipath degradation models requires the use of advanced methods such as Deep Learning. However, Deep Learning based mitigation methods tend to be hard to deploy due to a general lack of trust in their prediction due to their “black-box” behavior. This work tackles the notion of interpretability and generalization of multipath degradation models obtained using Auto-Encoders. We demonstrate the ability of Auto-Encoders to generate interpretable representations and to generalize to unseen situations.
Keywords: GNSS; Deep Learning; multipath; Self-Supervised Learning; Auto-Encoder; interpretability GNSS; Deep Learning; multipath; Self-Supervised Learning; Auto-Encoder; interpretability

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MDPI and ACS Style

Barbero, T.; Matera, E.R.; Ekambi, B.; Chamard, J.; Ekambi, M. Toward an Interpretable Multipath Error Model from GNSS Observables Through the Application of Deep Learning. Eng. Proc. 2026, 126, 14. https://doi.org/10.3390/engproc2026126014

AMA Style

Barbero T, Matera ER, Ekambi B, Chamard J, Ekambi M. Toward an Interpretable Multipath Error Model from GNSS Observables Through the Application of Deep Learning. Engineering Proceedings. 2026; 126(1):14. https://doi.org/10.3390/engproc2026126014

Chicago/Turabian Style

Barbero, Thomas, Eustachio Roberto Matera, Bertrand Ekambi, Jeremy Chamard, and Mathieu Ekambi. 2026. "Toward an Interpretable Multipath Error Model from GNSS Observables Through the Application of Deep Learning" Engineering Proceedings 126, no. 1: 14. https://doi.org/10.3390/engproc2026126014

APA Style

Barbero, T., Matera, E. R., Ekambi, B., Chamard, J., & Ekambi, M. (2026). Toward an Interpretable Multipath Error Model from GNSS Observables Through the Application of Deep Learning. Engineering Proceedings, 126(1), 14. https://doi.org/10.3390/engproc2026126014

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