Qinertia PPP-AR: Performance Evaluation of a Multi Constellation PPP-AR for Ground Truth Generation and Survey

Alexis Guinamard, Alexia Le Quilliec

Abstract:	Mapping applications, such as hydrography, mobile mapping, aerial or UAV based mapping are more and more relying on GNSS technology since the democratization of RTK technology, and the use of inertial sensors. These applications require the best level of precision achievable with GNSS technology, aiming at a one centimeter level accuracy. Other users like researchers developing new navigation solutions, or developers working on autonomous vehicles, regularly need to process trajectories and compare them to a ground truth. Whether for trajectory generation or for reference making, the generation of a ground truth trajectory is always a complex problem that drives all analyses which are based on this ground truth. That’s why assessing the reliability of this reference trajectory is a critical question. This presentation will briefly introduce Qinertia post processing solution, and its new PPP-AR technology, dedicated to survey and ground truth generation, and propose a rigorous verification methodology. The PPP-AR solution will be compared to high performance local PPK solutions in both static and dynamic conditions, in a wide range of scenarios and locations. In addition, the reliability of the solution will be assessed using novel quality control indicators. Following test campaign, performance is evaluated to 2cm 2D and 3cm vertical at one sigma.
Published in:	Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025) September 8 - 12, 2025 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	1200 - 1211
Cite this article:	Guinamard, Alexis, Le Quilliec, Alexia, "Qinertia PPP-AR: Performance Evaluation of a Multi Constellation PPP-AR for Ground Truth Generation and Survey," Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland, September 2025, pp. 1200-1211. https://doi.org/10.33012/2025.20475
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