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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?

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  • 1Institute of Communications and Navigation, German Aerospace Center (DLR), 17235 Neustrelitz, Germany.

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Summary
This summary is machine-generated.

Robust statistics offer improved Global Navigation Satellite Systems (GNSS) single point positioning (SPP) by addressing outliers. This study adapts robust regression methods for accurate GNSS solutions despite erroneous measurements.

Keywords:
global navigation satellite systems (GNSS)multipathrobust statisticssingle point positioning (SPP)

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Area of Science:

  • Geomatics Engineering
  • Robust Statistics
  • Satellite Navigation

Background:

  • Least-squares (LS) adjustments are standard for navigation but fail with outliers.
  • Outliers in Global Navigation Satellite Systems (GNSS) measurements violate Gaussian noise assumptions.
  • This leads to inaccurate positioning solutions in real-world applications.

Purpose of the Study:

  • To explore robust statistics for reliable GNSS single point positioning (SPP).
  • To adapt popular robust regression techniques for GNSS data.
  • To provide a general methodology for robust GNSS positioning.

Main Methods:

  • Surveyed M-estimators, S-estimators, and MM-estimators for robust regression.
  • Adapted these estimators into a methodology for robust GNSS positioning.
  • Validated the approach using theoretical insights and experimental datasets.

Main Results:

  • Demonstrated the effectiveness of robust statistics in handling outliers in GNSS data.
  • Provided a framework for robust GNSS positioning that outperforms standard LS adjustments.
  • Detailed discussion on the performance and application of different robust methods.

Conclusions:

  • Robust statistical methods are essential for accurate GNSS SPP in the presence of outliers.
  • The proposed methodology offers a viable alternative to traditional LS adjustments for GNSS.
  • Further research can refine these robust techniques for enhanced navigation system reliability.