Enhancing LEO-GNSS Positioning in Urban Canyons: A Factor Graph Optimization Approach with StarLink Simulations

Extensive research has explored augmenting GNSS positioning with LEO satellites to enhance urban accuracy and reliability. However, effectively integrating LEO and GNSS constellations remains an open challenge. To address this, this paper proposes a scalable, open-source Factor Graph Optimization (FGO) framework for LEO-GNSS positioning, designed to seamlessly integrate additional sensors. By jointly optimizing pseudorange and Doppler shift measurements from both constellations, the framework aims to improve positioning accuracy and stability. Validation using complex urban canyon datasets from Hong Kong and simulated Starlink signals demonstrates notable accuracy improvements over GNSS-only systems. Furthermore, this study releases the first open-source FGO-based LEO-GNSS fusion positioning algorithm, providing a foundational platform for autonomous systems research.