TY - JOUR
T1 - Precision analysis of NARX-based vehicle positioning algorithm in GNSS disconnected area
AU - Lee, Yong
AU - Kwon, Jay Hyoun
N1 - Publisher Copyright:
© 2021 Korean Society of Surveying. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Deep learning
KW - GNSS blockages
KW - NARX
KW - Sensor fusion
KW - Vehicle positioning system
UR - http://www.scopus.com/inward/record.url?scp=85120302928&partnerID=8YFLogxK
U2 - 10.7848/ksgpc.2021.39.5.289
DO - 10.7848/ksgpc.2021.39.5.289
M3 - Article
AN - SCOPUS:85120302928
SN - 1598-4850
VL - 39
SP - 289
EP - 295
JO - Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
JF - Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
IS - 5
ER -