As 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.
A Machine Learning Based GNSS Performance Prediction for Urban Air Mobility Using Environment Recognition
03.10.2021
1486438 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility
DOAJ | 2024
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