Forecasting model of space weather-driven GNSS positioning performance
(Sprache: Englisch)
Space weather effects on Global Navigation Satellite System (GNSS) positioning performance are well-understood. However, a numerical model capable of forecasting the extent of GNSS positioning performance deterioration due to space weather, geomagnetic and...
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Space weather effects on Global Navigation Satellite System (GNSS) positioning performance are well-understood. However, a numerical model capable of forecasting the extent of GNSS positioning performance deterioration due to space weather, geomagnetic and ionospheric effects remains a scientific challenge. This monograph addresses the challenge through introduction of the space weather - GNSS positioning performance coupling model, and utilisation of selected machine learning methods for model development in selected scenario of quiet space weather conditions. Based on the assembled database of experimental observations, several forecasting models were developed using machine learning methods selected according to statistical properties of observations. Models were compared and their performance assessed from both the modelling and computational perspectives. Presented results contribute to the effort of generalised model development. The monograph will benefit scientists in thefields of machine learning, space weather and satellite navigation, GNSS receiver designers, and a growing population of interested GNSS users.
Autoren-Porträt von Mia Filic, Renato Filjar
Filic, MiaMia Filic, mag. inf. et math. MRIN: Studied computer science and mathematics at Department of mathematics, Faculty of Science, University of Zagreb, Croatia. Independent statistical and machine learning, satellite navigation and space weather scientist. She received USRI Young Scientist Award in 2018.
Bibliographische Angaben
- Autoren: Mia Filic , Renato Filjar
- 2018, 116 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 6139901189
- ISBN-13: 9786139901180
Sprache:
Englisch
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