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

Autor:	Mia Filić, Renato Filjar
Rok vydání:	2018
Předmět:	Data set GNSS applications Computer science Position (vector) QUIET Physics::Space Physics Linear model Feature selection Space weather Selection (genetic algorithm) Physics::Geophysics Remote sensing
Zdroj:	Scopus-Elsevier
ISSN:	2331-5954
DOI:	10.33012/2018.16016
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7231496f591b6358779a324034cd2bec https://doi.org/10.33012/2018.16016 Zobrazit plný text záznamu