Comparative analysis of blind tropospheric correction models in Ghana

The impact of the earth’s atmospheric layers, particularly the troposphere on Global Navigation satellite system (GNSS) signals has become a major concern in GNSS accurate positioning, navigation, surveillance and timing applications. For precise GNSS applications, tropospheric delay has to be mitig...

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Autores principales:	Osah S., Acheampong A. A., Fosu C., Dadzie I.
Formato:	article
Lenguaje:	EN
Publicado:	Sciendo 2021
Materias:	gnss multiple comparison test prediction models tropospheric delay vienna mapping functions Geodesy QB275-343
Acceso en línea:	https://doaj.org/article/d50de2a0f316499582154555ab88d8cc
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spelling	oai:doaj.org-article:d50de2a0f316499582154555ab88d8cc2021-12-05T14:10:52ZComparative analysis of blind tropospheric correction models in Ghana2081-994310.1515/jogs-2020-0104https://doaj.org/article/d50de2a0f316499582154555ab88d8cc2021-05-01T00:00:00Zhttps://doi.org/10.1515/jogs-2020-0104https://doaj.org/toc/2081-9943The impact of the earth’s atmospheric layers, particularly the troposphere on Global Navigation satellite system (GNSS) signals has become a major concern in GNSS accurate positioning, navigation, surveillance and timing applications. For precise GNSS applications, tropospheric delay has to be mitigated as accurately as possible using tropospheric delay prediction models. However, the choice of a particular prediction model can signifi-cantly impair the positioning accuracy particularly when the model does not suit the user’s environment. A performance assessment of these prediction models for a suitable one is very important. In this paper, an assessment study of the performances of five blind tropospheric delay prediction models, the UNB3m, EGNOS, GTrop, GPT2w and GPT3 models was conducted in Ghana over six selected Continuously Operating Reference Stations (CORS) using the 1˚x1˚ gridded Vienna Mapping Function 3 (VMF3) zenith tropospheric delay (ZTD) product as a reference. The gridded VMF3-ZTD which is generated for every six hours on the 1˚x1˚ grids was bilinearly interpolated both space and time and transferred from the grid heights to the respective heights of the CORS locations. The results show that the GPT3 model performed better in estimating the ZTD with an overall mean (bias: 2.05 cm; RMS: 2.53 cm), followed by GPT2w model (bias: 2.32cm; RMS: 2.76cm) and GTrop model (bias: 2.41cm; 2.82cm). UNB3m model (bias: 6.23 cm; RMS: 6.43 cm) and EGNOS model (bias: 6.70 cm; RMS: 6.89 cm) performed poorly. A multiple comparison test (MCT) was further performed on the RMSE of each model to check if there is significant difference at 5% significant level. The results show that the GPT3, GPT2w and GTrop models are significantly indifferent at 5% significance level indicating that either of these models can be employed to mitigate the ZTD in the study area, nevertheless, the choice of GPT3 model will be more preferable.Osah S.Acheampong A. A.Fosu C.Dadzie I.Sciendoarticlegnssmultiple comparison testprediction modelstropospheric delayvienna mapping functionsGeodesyQB275-343ENJournal of Geodetic Science, Vol 11, Iss 1, Pp 14-26 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gnss multiple comparison test prediction models tropospheric delay vienna mapping functions Geodesy QB275-343
spellingShingle	gnss multiple comparison test prediction models tropospheric delay vienna mapping functions Geodesy QB275-343 Osah S. Acheampong A. A. Fosu C. Dadzie I. Comparative analysis of blind tropospheric correction models in Ghana
description	The impact of the earth’s atmospheric layers, particularly the troposphere on Global Navigation satellite system (GNSS) signals has become a major concern in GNSS accurate positioning, navigation, surveillance and timing applications. For precise GNSS applications, tropospheric delay has to be mitigated as accurately as possible using tropospheric delay prediction models. However, the choice of a particular prediction model can signifi-cantly impair the positioning accuracy particularly when the model does not suit the user’s environment. A performance assessment of these prediction models for a suitable one is very important. In this paper, an assessment study of the performances of five blind tropospheric delay prediction models, the UNB3m, EGNOS, GTrop, GPT2w and GPT3 models was conducted in Ghana over six selected Continuously Operating Reference Stations (CORS) using the 1˚x1˚ gridded Vienna Mapping Function 3 (VMF3) zenith tropospheric delay (ZTD) product as a reference. The gridded VMF3-ZTD which is generated for every six hours on the 1˚x1˚ grids was bilinearly interpolated both space and time and transferred from the grid heights to the respective heights of the CORS locations. The results show that the GPT3 model performed better in estimating the ZTD with an overall mean (bias: 2.05 cm; RMS: 2.53 cm), followed by GPT2w model (bias: 2.32cm; RMS: 2.76cm) and GTrop model (bias: 2.41cm; 2.82cm). UNB3m model (bias: 6.23 cm; RMS: 6.43 cm) and EGNOS model (bias: 6.70 cm; RMS: 6.89 cm) performed poorly. A multiple comparison test (MCT) was further performed on the RMSE of each model to check if there is significant difference at 5% significant level. The results show that the GPT3, GPT2w and GTrop models are significantly indifferent at 5% significance level indicating that either of these models can be employed to mitigate the ZTD in the study area, nevertheless, the choice of GPT3 model will be more preferable.
format	article
author	Osah S. Acheampong A. A. Fosu C. Dadzie I.
author_facet	Osah S. Acheampong A. A. Fosu C. Dadzie I.
author_sort	Osah S.
title	Comparative analysis of blind tropospheric correction models in Ghana
title_short	Comparative analysis of blind tropospheric correction models in Ghana
title_full	Comparative analysis of blind tropospheric correction models in Ghana
title_fullStr	Comparative analysis of blind tropospheric correction models in Ghana
title_full_unstemmed	Comparative analysis of blind tropospheric correction models in Ghana
title_sort	comparative analysis of blind tropospheric correction models in ghana
publisher	Sciendo
publishDate	2021
url	https://doaj.org/article/d50de2a0f316499582154555ab88d8cc
work_keys_str_mv	AT osahs comparativeanalysisofblindtroposphericcorrectionmodelsinghana AT acheampongaa comparativeanalysisofblindtroposphericcorrectionmodelsinghana AT fosuc comparativeanalysisofblindtroposphericcorrectionmodelsinghana AT dadziei comparativeanalysisofblindtroposphericcorrectionmodelsinghana
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Comparative analysis of blind tropospheric correction models in Ghana

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