Quantifying the relationship between ionospheric ionization levels and GNSS positioning errors: insights from Global Ionospheric Maps

This thesis investigates the relationship between ionospheric ionization levels and GNSS positioning errors, focusing on data from the ALEX2 station between 2013 and 2019. Us ing single- and dual-frequency Precise Point Positioning (PPP), ENU positioning errors were analyzed in correlation with iono...

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Detalhes bibliográficos
Autor: Fadl, Hesham Mohamed Yehia
Formato: tesis de maestría
Fecha de publicación:2025
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/429935
Acesso em linha:https://hdl.handle.net/2117/429935
Access Level:acceso abierto
Palavra-chave:Ionosphere
Ionization
Artificial satellites in telecommunication
Global Positioning System
GNSS Positioning
Ionospheric Corrections
Space Weather
Precise Point Positioning
GIMs
Ionosfera
Ionització
Satèl·lits artificials en telecomunicació
Sistema de posicionament global
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Satèl·lits i ràdioenllaços
Descrição
Resumo:This thesis investigates the relationship between ionospheric ionization levels and GNSS positioning errors, focusing on data from the ALEX2 station between 2013 and 2019. Us ing single- and dual-frequency Precise Point Positioning (PPP), ENU positioning errors were analyzed in correlation with ionospheric Total Electron Content (VTEC) derived from Global Ionospheric Maps (GIMs) and geomagnetic indices (Kp, ap). The analysis revealed that dual-frequency PPP mitigates ionospheric delays more effectively, resulting in improved accuracy compared to single-frequency PPP. Statistical correlations between positioning errors and ionospheric/geomagnetic indices highlighted significant temporal variations across daily, weekly, and monthly resolutions. Custom Python workflows, ad vanced GNSS processing with RTKLib, and space weather integration provided robust insights into ionospheric impacts on GNSS accuracy. The study aimed to develop a posi tioning error index that could be utilized in space weather applications, bridging the gap between GNSS error characterization and ionospheric monitoring.

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