Session A4c: Future Augmentation Systems, Correction Services and Integrity 2

GOOSE-VTL: GNSS/INS Deep Coupling with Fault Detection Strategy for Automotive Markets
Szu-Jung Wu, Katrin Dietmayer, Himanshu Gupta, Natalia Conde, Fraunhofer Institute for Integrated Circuits IIS; Mohamed Bochkati, University of the Bundeswehr Munich; Daniel Seybold, TeleOrbit GmbH; Thomas Pany, University of the Bundeswehr Munich; Matthias Overbeck, Fraunhofer Institute for Integrated Circuits IIS; Jürgen Seybold, TeleOrbit GmbH
Location: Holiday 6 (Second Floor)
Date/Time: Thursday, Sep. 11, 4:46 p.m.

In urban environments, the reliability of Global navigation satellite system (GNSS) is challenged by multipath, signal attenuation, and reduced satellite visibility, making standalone GNSS insufficient for automotive applications. Sensor fusion, particularly GNSS integrated with an inertial navigation system (INS), is therefore essential to ensure robust and continuous navigation. The GOOSE-VTL project, within the ESA’s Navigation Innovation and Support Program (NAVISP) Element 1 program of European Space Agency (ESA), developed and validated a proof-of-concept deep coupling (DC) GNSS/INS architecture tailored for urban scenarios. The approach integrates vector tracking with INS feedback in a single process, combining GNSS long-term stability with INS short-term accuracy. Integrity monitors, including carrier-to-noise density ratio (C/N0) checks, satellite elevation thresholds, Signal-Quality-Monitoring (SQM), and Kalman filter residuals, detect and exclude faulty or degraded signals prior to fusion. The architecture was assessed through simulations and real-world field tests. The results showed improved tracking stability, effective fault detection, and enhanced positioning accuracy compared to conventional scalar tracking. In semi-urban tests, the DC solution achieved a horizontal root mean square error (RMSE) of 4.54 m with 100% availability throughout the experiment. These results demonstrate that GNSS/INS deep coupling with integrated fault detection significantly enhances robustness and integrity in urban navigation.