engleski

Modelling the Relation between GNSS Positioning Performance Degradation, and Space Weather and Ionospheric Conditions using RReliefF Features Selection

The relationship between space weather and ionospheric conditions and GNSS position degradation has been recognized in numerous scientific studies. However, the relationship quantification remains a valuable scientific goal. In this manuscript, recent refinements in modelling of the level of GNSS positioning performance degradation caused by space weather and ionospheric dynamics are presented. The selected supervised machine learning (ML) method based on Linear Models (LM) and RReliefF variable selection process are used on experimentally collected data set in a quiet space-weather period.

RReliefF feature selection, GNSS positioning performance, space weather

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