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NAVIGATION: Journal of the Institute of Navigation

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Research ArticleOriginal Article
Open Access

Learning GNSS Positioning Corrections for Smartphones Using Graph Convolution Neural Networks

Adyasha Mohanty and Grace Gao
NAVIGATION: Journal of the Institute of Navigation December 2023, 70 (4) navi.622; DOI: https://doi.org/10.33012/navi.622
Adyasha Mohanty
Department of Aeronautics and Astronautics, Stanford University
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Grace Gao
Department of Aeronautics and Astronautics, Stanford University
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  • For correspondence: [email protected]
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Article Information

vol. 70 no. 4 navi.622
DOI 
https://doi.org/10.33012/navi.622

Published By 
Institute of Navigation
Print ISSN 
0028-1522
Online ISSN 
2161-4296
History 
  • Received October 3, 2022
  • Revision received July 19, 2023
  • Accepted September 15, 2023
  • Published online November 3, 2023.

Copyright & Usage 
© 2023 Institute of Navigation This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Author Information

  1. Adyasha Mohanty and
  2. Grace Gao⇑
  1. Department of Aeronautics and Astronautics, Stanford University
  1. Correspondence
    Grace Gao, Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA, 94305, Email: gracegao{at}stanford.edu
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NAVIGATION: Journal of the Institute of Navigation: 70 (4)
NAVIGATION: Journal of the Institute of Navigation
Vol. 70, Issue 4
Winter 2023
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Learning GNSS Positioning Corrections for Smartphones Using Graph Convolution Neural Networks
Adyasha Mohanty, Grace Gao
NAVIGATION: Journal of the Institute of Navigation Dec 2023, 70 (4) navi.622; DOI: 10.33012/navi.622

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Learning GNSS Positioning Corrections for Smartphones Using Graph Convolution Neural Networks
Adyasha Mohanty, Grace Gao
NAVIGATION: Journal of the Institute of Navigation Dec 2023, 70 (4) navi.622; DOI: 10.33012/navi.622
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  • Article
    • Abstract
    • 1 INTRODUCTION
    • 2 PROPOSED ALGORITHM
    • 3 EXPERIMENTS AND SETUP
    • 4 RESULTS
    • 5 ADDITIONAL EVALUATION
    • 6 CONCLUSIONS
    • HOW TO CITE THIS ARTICLE
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More in this TOC Section

  • GNSS L5/E5a Code Properties in the Presence of a Blanker
  • Robust Interference Mitigation in GNSS Snapshot Receivers
  • Identification of Authentic GNSS Signals in Time-Differenced Carrier-Phase Measurements with a Software-Defined Radio Receiver
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Keywords

  • AI
  • convolutions
  • GNSS
  • graph learning
  • machine learning
  • urban environment

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