Precision analysis of NARX-based vehicle positioning algorithm in GNSS disconnected area

Yong Lee, Jay Hyoun Kwon

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, owing to the development of autonomous vehicles, research on precisely determining the position of a moving object has been actively conducted. Previous research mainly used the fusion of GNSS/IMU (Global Positioning System / Inertial Navigation System) and sensors attached to the vehicle through a Kalman filter. However, in recent years, new technologies have been used to determine the location of a moving object owing to the improvement in computing power and the advent of deep learning. Various techniques using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and NARX (Nonlinear Auto-Regressive eXogenous model) exist for such learning-based positioning methods. The purpose of this study is to compare the precision of existing filter-based sensor fusion technology and the NARX-based method in case of GNSS signal blockages using simulation data. When the filter-based sensor integration technology was used, an average horizontal position error of 112.8 m occurred during 60 seconds of GNSS signal outages. The same experiment was performed 100 times using the NARX. Among them, an improvement in precision was confirmed in approximately 20% of the experimental results. The horizontal position accuracy was 22.65 m, which was confirmed to be better than that of the filter-based fusion technique.

Original languageEnglish
Pages (from-to)289-295
Number of pages7
JournalJournal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
Volume39
Issue number5
DOIs
StatePublished - 2021

Keywords

  • Deep learning
  • GNSS blockages
  • NARX
  • Sensor fusion
  • Vehicle positioning system

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