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

Author

Listed:
  • Maria Teresa Alonso

    (Research Group of Astronomy and GEomatics (gAGE), Universitat Politecnica de Catalunya—UPC, C/. Jordi Girona 1-3, Campus Nord, 08034 Barcelona, Spain
    The authors contributed equally to this work.)

  • Carlo Ferigato

    (European Commission, Joint Research Centre—JRC, via Enrico Fermi, 2749 21027 Ispra, Italy
    The authors contributed equally to this work.)

  • Deimos Ibanez Segura

    (Research Group of Astronomy and GEomatics (gAGE), Universitat Politecnica de Catalunya—UPC, C/. Jordi Girona 1-3, Campus Nord, 08034 Barcelona, Spain
    The authors contributed equally to this work.)

  • Domenico Perrotta

    (European Commission, Joint Research Centre—JRC, via Enrico Fermi, 2749 21027 Ispra, Italy
    The authors contributed equally to this work.)

  • Adria Rovira-Garcia

    (Research Group of Astronomy and GEomatics (gAGE), Universitat Politecnica de Catalunya—UPC, C/. Jordi Girona 1-3, Campus Nord, 08034 Barcelona, Spain
    The authors contributed equally to this work.)

  • Emmanuele Sordini

    (European Commission, Joint Research Centre—JRC, via Enrico Fermi, 2749 21027 Ispra, Italy
    The authors contributed equally to this work.)

Abstract

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.

Suggested Citation

  • Maria Teresa Alonso & Carlo Ferigato & Deimos Ibanez Segura & Domenico Perrotta & Adria Rovira-Garcia & Emmanuele Sordini, 2021. "Analysis of ‘Pre-Fit’ Datasets of gLAB by Robust Statistical Techniques," Stats, MDPI, vol. 4(2), pages 1-19, May.
    • Handle: RePEc:gam:jstats:v:4:y:2021:i:2:p:26-418:d:560909
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    References listed on IDEAS

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    1. Torti, Francesca & Perrotta, Domenico & Atkinson, Anthony C. & Riani, Marco, 2012. "Benchmark testing of algorithms for very robust regression: FS, LMS and LTS," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2501-2512.
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