Isik, Oguz KaganPetrunin, IvanInalhan, GokhanTsourdos, AntoniosMoreno, Ricardo VerdeguerGrech, Raphael2022-01-202022-01-202021-11-15Isik OK, Petrunin I, Inalhan G, et al., (2021) A machine learning based GNSS performance prediction for urban air mobility using environment recognition. In: 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), 3-7 October 2021, San Antonio, USA2155-7209https://doi.org/10.1109/DASC52595.2021.9594434https://dspace.lib.cranfield.ac.uk/handle/1826/17462As the primary navigation source, GNSS performance monitoring and prediction have critical importance for the success of mission-critical urban air mobility and cargo applications. In this paper, a novel machine learning based performance prediction algorithm is suggested considering environment recognition. Valid environmental parameters that support recognition and prediction stages are introduced, and K-Nearest Neighbour, Support Vector Regression and Random Forest algorithms are tested based on their prediction performance with using these environmental parameters. Performance prediction results and parameter importances are analyzed based on three types of urban environments (suburban, urban and urban-canyon) with the synthetic data generated by a high quality GNSS simulator.enAttribution-NonCommercial 4.0 InternationalGNSSmachine learningperformance predictionenvironment recognitionenvironment classificationintegrityurban air mobilityConference paper978-1-6654-3420-1