An Ambient Adaptive Global Navigation Satellite System Total Electron Content Predictive Model for Short-Term Rapid Geomagnetic Storm Events

Author:

Filjar Renato123ORCID,Heđi Ivan4ORCID,Prpić-Oršić Jasna5ORCID,Iliev Teodor36ORCID

Affiliation:

1. Department of Computer Engineering, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia

2. Laboratory for Data Mining, Open and Big Data, Centre for Artificial Intelligence and Cybersecurity, University of Rijeka, 51000 Rijeka, Croatia

3. Laboratory for Spatial Intelligence, Krapina University of Applied Sciences, 49000 Krapina, Croatia

4. Department of ICT, Virovitica University of Applied Sciences, 33000 Virovitica, Croatia

5. Department of Naval Architecture and Ocean Engineering, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia

6. Department of Telecommunications, University of Ruse, 7004 Ruse, Bulgaria

Abstract

Satellite navigation is an essential component of the national infrastructure. Space weather and ionospheric conditions are the prime sources of GNSS (global navigation satellite system) positioning, navigation, and timing (PNT) service disruptions and degradations. Protection, toughening, and augmentation (PTA) of GNSS PNT services require novel approaches in ionospheric effects mitigation. Standard global ionospheric correction models fail in the mitigation of high-dynamics and local ionospheric disturbances. Here, we demonstrate that in the case of the short-term fast-developing geomagnetic storm, a machine learning-based environment-aware GNSS ionospheric correction model for sub-equatorial regions may provide a substantial improvement over the existing global Klobuchar model, considered a benchmark. The proposed machine learning-based model utilises just the geomagnetic field density component observations as a predictor to estimate TEC/GNSS ionospheric delay as the prediction model target. Further research is needed to refine the methodology of machine learning model development selection and validation and to establish an architecture-agnostic framework for GNSS PTA development.

Funder

Croatian Science Foundation

Publisher

MDPI AG

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