Ref: CTALK-2023-0103

Correlation Analysis of GNSS Data Quality Indicators and Position Time Series using Machine-Learning Algorithms

Bamahry, Fikri ; Legrand, Juliette ; Bruyninx, Carine ; Pottiaux, Eric ; Fabian, Andras

Talk presented at EGU General Assembly 2023, Vienna, Austria on 2023-04-27

Abstract: The EUREF Permanent GNSS Network Central Bureau (EPN CB, www.epncb.eu [1]) monitors the quality of the daily GNSS observations of the EPN stations covering the period 1996-today. The associated data quality indicators (the number of observed versus expected observations in dual frequency, the lowest elevation cut-off observed, the number of missing epochs, the number of satellites, the number of maximum observations, and the number of cycle slips) are used to assess EPN stations' performance and as input for outlier detection in the daily position time series of the 400+ GNSS reference stations in Europe. Due to the increasing number of GNSS stations, the development of an automated algorithm to identify coordinate outliers caused by degraded GNSS data quality would allow to reduce the effort of human interpretation of the data quality indicators. We investigate the correlations between daily GNSS data quality metrics and daily position estimates to achieve this. This study assesses several machine-learning classification algorithms to find a suitable data-driven model based on the correlation between degraded GNSS data quality metrics and quality degradation in position time series. Based on this investigation, the Random Forest algorithm proved to be the most precise algorithm, with an Area Under the Receiver Operating Characteristic Curve (AUC) equal to 0.95, and a correct classification of almost 90% of the test dataset. The GNSS data quality indicators ‘maximum observations‘ and ‘cycle slips’ are also shown as the most important parameters driving this model, which is in accordance with the data quality criteria for GNSS stations that IGS has determined [2]. Our model will be implemented in our EPN CB operation routine to develop a new monitoring system that can detect quality degradation on the GNSS station that will enable to improve the reliability of the EPN reference frame product by detecting position outliers due to degraded GNSS data quality. Here, we will present the current development of this automated algorithm, the challenges we faced, and the preliminary results of this work.

Keyword(s): GNSS quality metrics ; GNSS position time series ; Machine learning
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The record appears in these collections:
Conference Contributions & Seminars > Conference Talks > Contributed Talks
Royal Observatory of Belgium > Reference Systems & Planetology

 Record created 2023-04-27, last modified 2023-04-27