Rate My Professor Hassan Elsayed

Elsayed's research specializations lie in GNSS positioning methodologies and integrity monitoring for autonomous vehicles and ground applications. In his doctoral work, he proposed the Two-Step Gaussian Bounding method for overbounding correlated double-differenced residuals in Network RTK to improve stochastic modeling. For Precise Point Positioning-RTK, he developed a fault partitioning method between network and user segments, enabling individual observation exclusion to reduce computational demands in fault detection and exclusion, validated with geodetic and multi-constellation commercial receivers. He integrated PPP-RTK with a classification robust adaptive Kalman filter, employing t-testing for epoch-wise weight adjustments and a sliding window autocorrelation-based adaptive unit weight variance for efficient fault de-weighting. Additionally, for ARAIM protection levels, he introduced subset clustering by normalized geometry mapping coefficients to derive multiple upper bounds per fault mode, achieving tighter protection levels and enhanced availability.

His key publications encompass "Fast Protection Level for Precise Positioning Using PPP-RTK with Robust Adaptive Kalman Filter" (Remote Sensing, 2025), "A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles" (Remote Sensing, 2025), "A new method for fault identification in real-time integrity monitoring of autonomous vehicles positioning using PPP-RTK" (GPS Solutions, 2023), "Bounding of correlated double-differenced GNSS observation errors using NRTK for precise positioning of autonomous vehicles" (Measurement, 2022), and his PhD thesis (2025). He also contributed to 3D modeling of Kyrenia shipwreck amphorae (Curtin HIVE, 2021). His scholarship has accumulated 33 citations and 658 reads on ResearchGate.