<div class="csl-bib-body">
<div class="csl-entry">Natras, R., Goss, A., Halilovic, D., Magnet, N., Mulić, M., Schmidt, M., & Weber, R. (2023). Regional Ionosphere Delay Models Based on CORS Data and Machine Learning. <i>NAVIGATION-JOURNAL OF THE INSTITUTE OF NAVIGATION</i>, <i>70</i>(3), Article navi. 577. https://doi.org/10.33012/navi.577</div>
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dc.identifier.issn
0028-1522
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/177702
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dc.description.abstract
The ionospheric refraction of GNSS signals can have an impact on positioning accuracy, especially in cases of single-frequency observations. Ionosphere models that are broadcasted by the satellite systems (e.g., Klobuchar, NeQuick-G) do not include enough details to permit them to correct single-frequency observations with sufficient accuracy. To address this issue, regional ionosphere models (RIMs) have been developed in several countries in the western Balkans based on dense Continuous Operating Reference Stations (CORS) observations. Subsequently, a RIM for the western Balkans was built using an artificial neural network that combined regional ionosphere parameters estimated from the CORS data with spatiotemporal (latitude, longitude, hour of day), solar (F10.7) and geomagnetic (Kp, Dst) parameters. The RIMs were tested at the solar maximum (March 2014), a geomagnetic storm (March 2015), and the solar minimum (March 2018). The new RIMs mimic the integrated electron density much more effectively than the Klobuchar model. Furthermore, RIMs significantly reduce the ionospheric effects on single-frequency positioning, indicating their neces-sity for use in positioning applications.
en
dc.language.iso
en
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dc.publisher
INST NAVIGATION - ION
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dc.relation.ispartof
NAVIGATION-JOURNAL OF THE INSTITUTE OF NAVIGATION
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
artificial neural network
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dc.subject
ionosphere delay modeling
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dc.subject
machine learning
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dc.subject
regional ionosphere model
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dc.subject
single-frequency positioning
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dc.subject
vertical total electron content
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dc.title
Regional Ionosphere Delay Models Based on CORS Data and Machine Learning