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APPLICATION OF LONG SHORT TERM MEMORY NEURAL NETWORKS FOR GPS SATELLITE CLOCK BIAS PREDICTION

Abstract

Satellite-based localization systems like GPS or Galileo are one of the most commonly used tools in outdoor navigation. While for most applications, like car navigation or hiking, the level of precision provided by commercial solutions is satisfactory it is not always the case for mobile robots. In the case of long-time autonomy and robots that operate in remote areas battery usage and access to synchronization data becomes a problem. In this paper, a solution providing a real-time onboard clock synchronization is presented. Results achieved are better than the current state-of-the-art solution in real-time clock bias prediction for most satellites.

Keywords:

neural networks, LSTM, time series prediction, clock bias, GNSS, machine learning

Details

Issue
Vol. 25 No. 4 (2021)
Section
Research article
Published
2021-12-30 — Updated on 2022-03-04
Versions
DOI:
https://doi.org/10.34808/tq2021/25.4/a
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors

  • PAWEŁ PRZESTRZELSKI

    Department of Computer Science Polish-Japanese Academy of Information Technology Koszykowa 86, 02-008 Warsaw, Poland
  • PIOTR GNYŚ

    Department of Computer Science Polish-Japanese Academy of Information Technology Koszykowa 86, 02-008 Warsaw, Poland

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