Analysis of ‘Pre-Fit’ Datasets of gLAB by Robust Statistical Techniques

• Main Authors: Maria Teresa Alonso, Carlo Ferigato, Deimos Ibanez Segura, Domenico Perrotta, Adria Rovira-Garcia, Emmanuele Sordini
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Stats
• Subjects: GNSS positioning; robust statistics; GNSS LABoratory—gLAB; Flexible Statistics and Data Analysis toolbox—FSDA
• Online Access: https://www.mdpi.com/2571-905X/4/2/26

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 <i>solution</i> of the problem of determining a position by means of GNSS measurements. The present work aimed to improve the <i>pre-fit outlier detection</i> function of gLAB since <i>outliers</i>, if undetected, deteriorate the obtained position coordinates. The methodology exploits <i>robust statistical tools</i> 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 <i>pre-fit outlier detection</i> function. This study concludes that robust statistical analysis techniques, when applied to the <i>pre-fit</i> layer of gLAB, improve the overall reliability and accuracy of the positioning solution.