Network-based ionospheric gradient monitoring to support GBAS
Large ionospheric gradients acting between a Ground Based Augmentation System (GBAS) reference station and an aircraft on approach could lead to hazardous position errors if undetected. Current GBAS stations provide solutions against this threat that rely on the use of “worst-case” conservative thre...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/365754 |
| Acceso en línea: | https://hdl.handle.net/2117/365754 https://dx.doi.org/10.1002/navi.411 |
| Access Level: | acceso abierto |
| Palabra clave: | Mobile geographic information systems Artificial satellites in navigation Galileo satellite navigation system Ground Based Augmentation System (SBAS) Ionospheric gradients Monitoring network Sistemes de Posicionament Global Satèl·lits artificials en navegació Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| Sumario: | Large ionospheric gradients acting between a Ground Based Augmentation System (GBAS) reference station and an aircraft on approach could lead to hazardous position errors if undetected. Current GBAS stations provide solutions against this threat that rely on the use of “worst-case” conservative threat models, which could limit the availability of the system. This paper presents a methodology capable of detecting ionospheric gradients in real time and estimating the actual threat model parameters based on a network of dual-frequency and multi-constellation GNSS monitoring stations. First, we evaluate the performance of our algorithm with synthetic gradients that are simulated over the nominal measurements recorded by a reference network in Alaska. Afterwards, we also assess it with one real ionospheric gradient measured by the same network. Results with both simulated gradients and a real gradient show the potential to support GBAS by detecting and estimating these gradients instead of always using “worst-case” models. |
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