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NAV Lab wins seven "Best Presentation of the Session Awards" at ION GNSS+ conference
ION GNSS+ is the world's largest annual conference on GNSS organized by Institute of Navigation (ION). Our NAV Lab has another fruitful year of attending ION GNSS+, showcasing twelve presentations, fourteen papers and winning seven "Best Presentation of the Session Awards" for the following papers.
- Ashwin V. Kanhere and Grace Gao, Fault-Robust GPS Spoofing Mitigation with Expectation-Maximization, Proceedings of the Institute of Navigation GNSS+ conference (ION GNSS+ 2023), Denver, CO, Sep 2023.
- Asta Wu, Adyasha Mohanty, Anonto Zaman, and Grace Gao, Bounding GPS-Based Positioning and Navigation Uncertainty for Autonomous Drifting via Reachability, Proceedings of the Institute of Navigation GNSS+ conference (ION GNSS+ 2023), Denver, CO, Sep 2023.
- Adyasha Mohanty and Grace Gao, Tightly Coupled Graph Neural Network and Kalman Filter for Improving Smartphone GNSS Positioning, Proceedings of the Institute of Navigation GNSS+ conference (ION GNSS+ 2023), Denver, CO, Sep 2023.
- Daniel Neamati, Shubh Gupta, Mira Partha, and Grace Gao, Neural City Maps for GNSS NLOS Prediction, Proceedings of the Institute of Navigation GNSS+ conference (ION GNSS+ 2023), Denver, CO, Sep 2023.
- Derek Knowles and Grace Gao, Detection and Exclusion of Multiple Faults using Euclidean Distance Matrices, Proceedings of the Institute of Navigation GNSS+ conference (ION GNSS+ 2023), Denver, CO, Sep 2023.
- Mira Partha, Shubh Gupta, and Grace Gao, Neural City Maps: A Case for 3D Urban Environment Representations Based on Radiance Fields, Proceedings of the Institute of Navigation GNSS+ conference (ION GNSS+ 2023), Denver, CO, Sep 2023.
- Marta Cortinovis, Keidai Iiyama, and Grace Gao, Satellite Ephemeris Approximation Methods to Support Lunar Positioning, Navigation, and Timing Services, Proceedings of the Institute of Navigation GNSS+ conference (ION GNSS+ 2023), Denver, CO, Sep 2023.
Congratulations, Ashwin, Asta, Adyasha, Daniel, Derek, Mira, Marta and the co-authors: Anonto, Shubh, and Keidai! Thanks to all the NAV Lab members for providing valuable feedback for presentation dry runs and reviewing the papers.