Improvement of GPS and BeiDou extended orbit predictions with CNNs

Jaakko Pihlajasalo, Helena Leppäkoski, Simo Ali-Löytty, Robert Piché

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

    3 Citations (Scopus)
    200 Downloads (Pure)


    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.

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


    ConferenceEuropean Navigation Conference
    Period20/09/18 → …

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Signal Processing
    • Control and Optimization


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