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Evaluating the Static Relative Positioning Accuracy of a GPS Equipment by Linear Models

  • M. Filomena TeodoroEmail author
  • Fernando M. Gonçalves
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 505)

Abstract

The processing of baselines with considerable length may not be successful due several problems, for example, the ionospheric and tropospheric delays estimates are not adequate. To reduce this problem, there exists some models to minimize the biases. The first-order ionospheric biases can be reduced by \(98\%\) taking the combination of \(L_1\) and \(L_2\) carrier-phase. The equipment under evaluation uses this solution to the most baselines considered in our work. Still is necessary to reduce the tropospheric bias. An improved and advanced tropospheric bias mitigation strategy is used as alternative to a simpler one. The reduction of bias is verified and quantified using the rate of successful baselines processed by the GPS equipment which uses an improved strategy with a zenith tropospheric scale factor per station. We have built some models by general linear models to evaluate the performance of the equipment. We are aware that 1D and 2D present different behaviors, we analyzed both cases individually with each strategy. In this article, we present partially such analysis for 2D case.

Keywords

Baselines Bias General linear models Performance GPS equipment 

Notes

Acknowledgements

This work was supported by Portuguese funds through the FCT, Center for Computational and Stochastic Mathematics (CEMAT), University of Lisbon, Portugal, project UID/Multi/04621/2013, and Center of Naval Research (CINAV), Naval Academy, Portuguese Navy, Portugal.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • M. Filomena Teodoro
    • 1
    • 2
    Email author
  • Fernando M. Gonçalves
    • 3
  1. 1.CINAV, Portuguese Naval Academy, Portuguese NavyAlmadaPortugal
  2. 2.CEMAT - Center for Computational and Stochastic Mathematics, Instituto Superior TécnicoLisbon UniversityLisboaPortugal
  3. 3.NGI, Nottingham Geospatial InstituteUniversity of NottinghamNottinghamUK

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