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Helmert Variance Component Estimation for Multi-GNSS Relative Positioning.

Mowen Li1, Wenfeng Nie1,2,3, Tianhe Xu1

  • 1Institute of Space Sciences, Shandong University, 180 Wenhuaxi Road, Weihai 264209, China.

Sensors (Basel, Switzerland)
|January 30, 2020
PubMed
Summary
This summary is machine-generated.

Helmert variance component estimation (HVCE) improves Multi-Global Navigation Satellite System (Multi-GNSS) positioning accuracy. Freezing variances (FVUW) significantly reduces computation time while maintaining high accuracy for kinematic relative positioning.

Keywords:
Helmert variance component estimation (HVCE)Multi-GNSSrelative positioningweighting strategy

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Area of Science:

  • Geodesy and Geomatics
  • Satellite Navigation Systems

Background:

  • Multi-constellation Global Navigation Satellite System (Multi-GNSS) is standard for high-accuracy positioning.
  • Noise in code and phase measurements varies by GNSS constellation.
  • Helmert variance component estimation (HVCE) adjusts constellation contributions but is computationally intensive.

Purpose of the Study:

  • To evaluate HVCE posterior weighting for kinematic relative Multi-GNSS positioning.
  • To assess the stability of observation weight proportions over time.
  • To develop a computationally efficient method for accurate Multi-GNSS positioning.

Main Methods:

  • Kinematic relative Multi-GNSS positioning experiment using six short baselines.
  • Application of HVCE posterior weighting strategy.
  • Development and testing of a frozen variances of unit weight (FVUW) approach.

Main Results:

  • HVCE posterior weighting improved positioning accuracy by up to 20.5% in ENU components compared to elevation-dependent (ED) weighting.
  • Weight proportions for code and phase observations remained stable over 30 days.
  • FVUW approach achieved similar accuracy improvements (up to 20.0%) while reducing computation time by 88%.

Conclusions:

  • HVCE posterior weighting enhances Multi-GNSS positioning accuracy.
  • Frozen variances of unit weight (FVUW) offer a computationally efficient alternative without sacrificing accuracy.
  • FVUW demonstrates effectiveness for extended periods, indicating its practical applicability.