CEAS EuroGNC 2026
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Evolution of Robust Navigation Towards the Aerial Environment
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| Santiago García Samartín |
Homeland Security & Defense GMV, Madrid, Spain. | | Javier Ferrero Micó |
Homeland Security & Defense GMV, Madrid, Spain. | | Mikel Loinaz Anton |
Homeland Security & Defense GMV, Madrid, Spain. | | Carmen María Haro Montero |
NDRA Group, Madrid, Spain. | | Joao Vieira Caetano |
European Defence Agency, Single European Sky Unit, Brussels, Belgium. |
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| Abstract:
Unmanned Aircraft Systems (UAS) flights rely on the ability to determine their position with high accuracy and continuity, even in degraded environments. Traditional localisation approaches combine data from Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMUs), which together provide reliable navigation under nominal conditions. However, the increasing sophistication of jamming and spoofing threats has exposed the dependence of these systems on external signals, creating a demand for alternative methods that can ensure precise navigation when GNSS data becomes unreliable or unavailable. To address this challenge, this work explores the integration of classical navigation sensors with artificial intelligence techniques to enhance precision in navigation, as well as robustness in complex environments. The proposed framework combines IMU and GNSS information with visual data processed through deep learning algorithms for odometry estimation and map correlation. All measurements are subsequently fused within an Extended Kalman Filter (EKF), which provides an optimal estimation of the vehicle state and dynamically balances sensor contributions according to their estimated reliability. The resulting system enables UAS to adaptively select the most accurate and stable source of navigation data depending on mission context, terrain visibility, and environmental conditions. Beyond the technical contribution, this approach aims to reduce operational dependency on external infrastructure while improving safety in autonomous flight missions. The proposed architecture is systematically evaluated by comparing different sensor configurations, using the classical GNSS+IMU solution as a reference baseline. This controlled assessment allows the contribution and limitations of visual aiding to be clearly quantified relative to standard navigation performance. The results demonstrate the feasibility of deploying visual-aided navigation as a resilient complementary component within small UAS Positioning, Navigation, and Timing (PNT) architectures, while identifying robustness to visual outliers as a key avenue for further performance enhancement.
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| Keywords: UAS; Deep learning; visual navigation; self-localization; GNSS denied environments |
 View PDF CEAS-GNC-2026-040 doi: 10.82124/CEAS-GNC-2026-040 |
| Santiago García Samartín, Javier Ferrero Micó, Mikel Loinaz Anton, Carmen María Haro Montero, Joao Vieira Caetano: Evolution of Robust Navigation Towards the Aerial Environment. Proceedings of the 2026 CEAS EuroGNC conference. Madrid, Spain. May 2026. doi: 10.82124/CEAS-GNC-2026-040.
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| BibTeX entry (UTF-8):
@Incollection{CEAS-GNC-2026-040,
author = {García Samartín, Santiago and Ferrero Micó, Javier and Loinaz Anton, Mikel and Montero, Carmen María Haro and Caetano, Joao Vieira},
title = {Evolution of Robust Navigation Towards the Aerial Environment},
booktitle = {Proceedings of the 2026 {CEAS EuroGNC} conference},
address = {Madrid, Spain},
month = may,
year = {2026},
doi = {10.82124/CEAS-GNC-2026-040}
}
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