Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?

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.