Application of Machine Learning for Evaluation of GNSS Processing Protocols

The Center for Orbit Determination in Europe (CODE) hosts one of the global analysis centers of the International GNSS Service (IGS). Each day, the data of about 250 GNSS stations are processed in a highly automated manner. The processing protocols contain many quality parameters related to station coordinates, satellite orbits, and satellite/receiver clock corrections.

In the context of a reprocessing campaign, 25 years of GNSS measurements are analysed within a short timeframe and therefore a huge number of processing protocols are generated. A manual inspection of all these protocols is highly time consuming. Machine learning (ML) represents a promising approach to provide a data driven and objective evaluation of these protocols. The main objective of a ML framework is to analyse a big number of independent quality parameters in order to automatically detect individual days with problems in the data analysis. Furthermore, we expect that ML could contribute to the detection of unexpected systematics in the solutions and has the potential to improve the GNSS analysis strategy.

As a first step, we have focused on one aspect of the processing protocols, namely the orbit misclosures (discontinuities at the end of the orbital arcs) at midnight. It is known that the orbit modelling of GNSS satellites is more difficult during eclipse seasons. In order to assess the capabilities of different machine learning algorithms for our purpose, we have evaluated the magnitude of the orbit misclosures and have tried to recover the information on whether the satellite was passing the earth shadow or not. State-of-the-art ML algorithms (Random Forest and Decision Tree) showed promising results of up to 80% success rate.