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Methods for long-term GNSS clock offset prediction

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2019 International Conference on Localization and GNSS, ICL-GNSS 2019
EditorsElena-Simona Lohan, Alexander Rugamer, Jari Nurmi, Wolfgang Koch, Albert Heuberger
PublisherIEEE
ISBN (Electronic)9781728124452
DOIs
Publication statusPublished - 1 Jun 2019
Publication typeA4 Article in a conference publication
EventInternational Conference on Localization and GNSS - Nuremberg, Germany
Duration: 4 Jun 20196 Jun 2019

Conference

ConferenceInternational Conference on Localization and GNSS
CountryGermany
CityNuremberg
Period4/06/196/06/19

Abstract

Clock offset predictions along with satellite orbit predictions are used in self-assisted GNSS to reduce the Time-to-First-Fix of a satellite positioning device. This paper compares three methods for predicting GNSS satellite clock offsets: polynomial regression, Kalman filtering and support vector machines (SVM). The regression polynomial and support vector machine model are trained from past offsets. The Kalman filter uses past offsets to estimate the clock offset coefficients. In tests with GPS and GLONASS data, it is found that all three methods significantly improve the clock predictions relative to extrapolation with the basic clock model of the last obtained broadcast ephemeris (BE). In particular, the 68% quantile of 7 day clock offset errors of GPS satellites was reduced by 66% with polynomial regression, 69% with Kalman filtering and 56% with SVM on average.