Organizers:
- Felix Ott, Fraunhofer IIS, Germany
- Tobias Feigl, Fraunhofer IIS & Friedrich-Alexander Universität Erlangen-Nürnberg, Fermany
- Alexander Rügamer, Fraunhofer IIS, Germany
- Christopher Mutschler, Fraunhofer IIS & UTN Nürnberg, Germany
Rationale and Scope:
This special session explores the growing role of Artificial Intelligence (AI) in GNSS technologies, signal processing, receiver design, and application-level systems. It aims to provide a forum for discussing where AI can offer tangible benefits across the GNSS processing chain, from signal-level analysis and receiver functions to positioning algorithms, error mitigation, and hybrid integration with complementary sensors and signals.
The session welcomes contributions on both methodological advances and application-driven studies that demonstrate how AI can enhance GNSS components, system performance, robustness, and operational capabilities in real-world scenarios. It is intended to bring together researchers working across different layers of GNSS technology, from signal processing and receiver architectures to positioning engines and hybrid system integration. Contributions may address algorithms, datasets, benchmarking approaches, hardware implementations, and experimental validations in practical deployment scenarios.
AI is increasingly being explored throughout the GNSS processing chain, from low-level signal analysis to high-level positioning and system adaptation. Recent work has shown promising results in areas such as interference and spoofing detection, multipath and NLOS classification, adaptive measurement weighting, hybrid sensor fusion, and data-driven support for receiver and navigation functions. At the same time, the broader question remains open as to where AI truly adds value in GNSS technologies, which problems benefit most from learning-based approaches, and how such methods can be integrated into practical and trustworthy GNSS systems.
This topic connects several active research directions that are currently spread across different research domains. Rather than focusing only on robust PNT or interference mitigation, the session provides a broader forum for AI applications in GNSS technologies and components, including signal processing, receiver functions, error mitigation, system adaptation, and hybrid localization.
In this Special Session, we invite authors to submit papers related (but not limited) to:
• AI for GNSS signal processing, acquisition, and tracking
• Learning-based interference, jamming, spoofing, and anomaly detection
• AI for multipath, NLOS, and adverse propagation mitigation
• Data-driven measurement quality assessment, satellite selection, and weighting
• AI-enhanced receiver technologies and adaptive GNSS processing architectures
• Machine learning for positioning, navigation, and timing estimation, incl. direct position estimation
• AI for hybrid localization using GNSS with inertial sensors, communications signals, or signals of opportunity
• Trustworthy, explainable, uncertainty-aware, and resource-efficient AI for GNSS systems
The paper submission to this special sesssion can be done at https://www.conftool.pro/icl-gnss2026.