Robust GPS/Galileo/GLONASS Data Fusion Using Extended Kalman Filter

Abstract

This paper presents data fusion from multiple Global Navigation Satellite System (GNSS) constellations. GNSS brings more signals and more satellites to improve the accuracy of user’s position. However, multiple failures in satellite’s signals sometimes negatively impact the determination of the user’s position and should be considered. For this purpose, the present paper provides robust Extended Kalman Filter (robust-EKF) to eliminate the outliers. The algorithms are tested by using GPS, Galileo and GLONASS data corresponding on data from base station GRAC in Grasse, France. Applying the robust-EKF method as well as the robust combination of GPS, Galileo, and GLONASS data improves the position accuracy by about 30.0%, 20.7%, and 90% compared to the use of GPS data only, Galileo data only, and GLONASS data only, respectively, and by about 67% compared to the nonrobust combination of GPS, Galileo, and GLONASS data.