PT  - JOURNAL ARTICLE
AU  - Natras, Randa
AU  - Goss, Andreas
AU  - Halilovic, Dzana
AU  - Magnet, Nina
AU  - Mulic, Medzida
AU  - Schmidt,, Michael
AU  - Weber, Robert
TI  - Regional Ionosphere Delay Models Based on CORS Data and Machine Learning
AID  - 10.33012/navi.577
DP  - 2023 Sep 21
TA  - NAVIGATION: Journal of the Institute of Navigation
PG  - navi.577
VI  - 70
IP  - 3
4099  - https://navi.ion.org/content/70/3/navi.577.short
4100  - https://navi.ion.org/content/70/3/navi.577.full
SO  - NAVIGATION2023 Sep 21; 70
AB  - 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 necessity for use in positioning applications.