Analysis of ‘Pre-Fit’ datasets of gLAB by robust statistical techniques
The GNSS LABoratory tool (gLAB) is an interactive educational suite of applications for processing data from the Global Navigation Satellite System (GNSS). gLAB is composed of several data analysis modules that compute the solution of the problem of determining a position by means of GNSS measuremen...
| 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:2117/350752 |
| Acceso en línea: | https://hdl.handle.net/2117/350752 https://dx.doi.org/10.3390/stats4020026 |
| Access Level: | acceso abierto |
| Palabra clave: | Global Positioning System Remote sensing--Data processing GNSS positioning Robust statistics GNSS LABoratory Flexible statistics and data analysis toolbox Sistema de posicionament global Àrees temàtiques de la UPC::Aeronàutica i espai::Sistemes CNS/ATM (Communication, Navigation, Surveillance/Air Traffic Management) |
| Sumario: | The GNSS LABoratory tool (gLAB) is an interactive educational suite of applications for processing data from the Global Navigation Satellite System (GNSS). gLAB is composed of several data analysis modules that compute the solution of the problem of determining a position by means of GNSS measurements. The present work aimed to improve the pre-fit outlier detection function of gLAB since outliers, if undetected, deteriorate the obtained position coordinates. The methodology exploits robust statistical tools for regression provided by the Flexible Statistics and Data Analysis (FSDA) toolbox, an extension of MATLAB for the analysis of complex datasets. Our results show how the robust analysis FSDA technique improves the capability of detecting actual outliers in GNSS measurements, with respect to the present gLAB pre-fit outlier detection function. This study concludes that robust statistical analysis techniques, when applied to the pre-fit layer of gLAB, improve the overall reliability and accuracy of the positioning solution. |
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