GNSS technologies typically suffer significant accuracy issues in urban environments due to signal degradation caused by obstacles like buildings which block and reflects GNSS signals leading to big positioning errors. The most dangerous impairments are due to specularly reflected Non-Line-Of-Sight (srNLOS) signals. Such errors can be attenuated through the application of the Intelligent Urban Positioning (IUP) concept. This project aims to develop a system based on IUP concept to improve GNSS positioning in urban environments by exploiting srNLOS signals. Key developments include:

i                      3D mapping algorithms for submeter-accurate urban models,

ii                     srNLOS signal prediction and real-time detection algorithms, and

iii                   srNLOS correction applications to enhance position accuracy. Validation of the system involves software simulations and field tests using GNSS/INS systems.

The system targets cost-effective, high-accuracy solutions for automotive navigation and autonomous driving, reducing urban positioning errors to under 2 meters with modular, cloud-based adaptability.