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Improvement of GPS and BeiDou extended orbit predictions with CNNs

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

Details

Original languageEnglish
Title of host publication26th European Navigation Conference, ENC 2018
Subtitle of host publicationGothenburg, Sweden, 14-17 May, 2018
PublisherIEEE
Pages54-59
Number of pages6
ISBN (Print)9781538649626
DOIs
Publication statusPublished - 10 Aug 2018
Publication typeA4 Article in a conference publication
EventEuropean Navigation Conference -
Duration: 20 Sep 2018 → …

Conference

ConferenceEuropean Navigation Conference
Period20/09/18 → …

Abstract

This paper presents a method for improving the accuracy of extended GNSS satellite orbit predictions with convolutional neural networks (CNN). Satellite orbit predictions are used in self-assisted GNSS to reduce the Time to First Fix of a satellite positioning device. We describe the models we use to predict the satellite orbit and present the improvement method that uses CNN. The CNN estimates future prediction errors of our model and these estimates are used to correct our orbit predictions. We also describe how the neural network can be implemented into our prediction algorithm. In tests with GPS and BeiDou data, the method significantly improves orbit prediction accuracy. For example, the 68% error quantile of 7 day orbit prediction errors of GPS satellites was reduced by 45% on average.

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