Pillar lead: Penggao Yan
Accuracy is not enough for safety-critical navigation: a confident answer in the wrong place is more dangerous than an honest “I don’t know.” This pillar builds systems that estimate not only where they are, but whether that answer can be trusted — outputting a state estimate and a quantified protection level together.
We develop fault detection, non-Gaussian error bounding, protection-level computation, and integrity-constrained optimization. Representative threads: Principal Gaussian Overbound for Heavy-tailed Error Bounding (Yan, Zhong, Hsu, IEEE TAES 2024), Integrity-Constrained Factor Graph Optimization (Xia, Wen, Hsu, NAVIGATION 2024), and Fault Detection for Gaussian Mixture Noises in Lidar/IMU Integrated Localization (Yan et al., NAVIGATION 2025).
This pillar is the technical core of the 2023 ION Per Enge Early Achievement Award and the RGC Research Impact Fund R5009-21 (HK$4.43M total (HK$3.1M RGC), Project Coordinator, 2021–2026).
Lab Contributors
Related Projects
- Multi-Agent Collaborative GNSS for Intelligent Transportation Systems completed
- Robust GNSS Navigation for Urban Air Traffic Management active
- GraphGNSSLib active
- Universal Vehicle Self-Diagnosis: Predictive Component Monitoring active
- 3D Map-Aided GNSS (3DMA) for Smartphone and IoT active
- Environment-Aware GNSS for UAS and Autonomous Driving active
- GPS_VT_SDR: Receiver-Level GNSS Signal Processing Platform archived-influential