Analysis of the Spatial Distribution and Common Mode Error Correlation in a Small-Scale GNSS Network

Area of Science:

• Geodesy
• Geophysics
• Signal Processing

Background:

• Common mode errors (CME) in Global Navigation Satellite System (GNSS) data obscure crustal movement signals.
• Accurate velocity estimates of station coordinates are vital for reliable geodetic measurements.
• Current CME separation methods using signal filtering often neglect spatial correlations between stations.

Purpose of the Study:

• To improve the accuracy of GNSS time series analysis by developing a novel method for common mode error separation.
• To address the limitations of existing methods by incorporating spatial information of GNSS networks.
• To enhance the precision and reliability of station positioning by mitigating CME impacts.

Main Methods:

• Improvement of the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors.
• Development of the Weighted Independent Component Analysis (WICA) method to account for the spatial distribution of GNSS stations.
• Decomposition of observation network residuals to identify and separate CME signals.

Main Results:

• The WICA method reduced the root mean square (RMS) of coordinate time series by an average of 27.96% (East), 15.23% (North), and 28.33% (Up).
• Compared to standard ICA, WICA showed improvements of 12.53% (East), 3.70% (North), and 8.97% (Up).
• The results demonstrate WICA's effectiveness in separating common mode errors.

Conclusions:

• The Weighted Independent Component Analysis (WICA) method provides a more accurate approach to common mode error separation in GNSS data.
• Incorporating spatial correlation of stations significantly enhances CME separation compared to traditional ICA.
• This study offers a new algorithmic solution for improving the precision of geodetic measurements and crustal movement analysis.