Online Calibration of Inertial Sensors Based on Error Backpropagation

Global satellite navigation systems (GNSSs) are the most-used technology for the localization of vehicles in the outdoor environment, but in the case of a densely built-up area or during passage through a tunnel, the satellite signal is not available or has poor quality. Inertial navigation systems...

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Published in	Sensors (Basel, Switzerland) Vol. 24; no. 23; p. 7525
Main Authors	Simak, Vojtech, Andel, Jan, Nemec, Dusan, Kekelak, Juraj
Format	Journal Article
Language	English
Published	Switzerland MDPI AG 25.11.2024 MDPI
Subjects	Accelerometers Accuracy Algorithms Artificial intelligence Calibration Communication Communications equipment Electronics in navigation error backpropagation global satellite navigation Inertial navigation Inertial navigation (Aeronautics) inertial sensors Localization Microelectromechanical systems Neural networks online calibration Receivers & amplifiers Sensors Technology application Wireless access points inertial sensors error backpropagation online calibration global satellite navigation
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Abstract	Global satellite navigation systems (GNSSs) are the most-used technology for the localization of vehicles in the outdoor environment, but in the case of a densely built-up area or during passage through a tunnel, the satellite signal is not available or has poor quality. Inertial navigation systems (INSs) allow localization dead reckoning, but they have an integration error that grows over time. Inexpensive inertial measurement units (IMUs) are subject to thermal-dependent error and must be calibrated almost continuously. This article proposes a novel method of online (continuous) calibration of inertial sensors with the aid of the data from the GNSS receiver during the vehicle’s route. We performed data fusion using an extended Kalman filter (EKF) and calibrated the input sensors through error backpropagation. The algorithm thus calibrates the INS sensors while the GNSS receiver signal is good, and after a GNSS failure, for example in tunnels, the position is predicted only by low-cost inertial sensors. Such an approach significantly improved the localization precision in comparison with offline calibrated inertial localization with the same sensors.
AbstractList	Global satellite navigation systems (GNSSs) are the most-used technology for the localization of vehicles in the outdoor environment, but in the case of a densely built-up area or during passage through a tunnel, the satellite signal is not available or has poor quality. Inertial navigation systems (INSs) allow localization dead reckoning, but they have an integration error that grows over time. Inexpensive inertial measurement units (IMUs) are subject to thermal-dependent error and must be calibrated almost continuously. This article proposes a novel method of online (continuous) calibration of inertial sensors with the aid of the data from the GNSS receiver during the vehicle’s route. We performed data fusion using an extended Kalman filter (EKF) and calibrated the input sensors through error backpropagation. The algorithm thus calibrates the INS sensors while the GNSS receiver signal is good, and after a GNSS failure, for example in tunnels, the position is predicted only by low-cost inertial sensors. Such an approach significantly improved the localization precision in comparison with offline calibrated inertial localization with the same sensors. Global satellite navigation systems (GNSSs) are the most-used technology for the localization of vehicles in the outdoor environment, but in the case of a densely built-up area or during passage through a tunnel, the satellite signal is not available or has poor quality. Inertial navigation systems (INSs) allow localization dead reckoning, but they have an integration error that grows over time. Inexpensive inertial measurement units (IMUs) are subject to thermal-dependent error and must be calibrated almost continuously. This article proposes a novel method of online (continuous) calibration of inertial sensors with the aid of the data from the GNSS receiver during the vehicle's route. We performed data fusion using an extended Kalman filter (EKF) and calibrated the input sensors through error backpropagation. The algorithm thus calibrates the INS sensors while the GNSS receiver signal is good, and after a GNSS failure, for example in tunnels, the position is predicted only by low-cost inertial sensors. Such an approach significantly improved the localization precision in comparison with offline calibrated inertial localization with the same sensors.Global satellite navigation systems (GNSSs) are the most-used technology for the localization of vehicles in the outdoor environment, but in the case of a densely built-up area or during passage through a tunnel, the satellite signal is not available or has poor quality. Inertial navigation systems (INSs) allow localization dead reckoning, but they have an integration error that grows over time. Inexpensive inertial measurement units (IMUs) are subject to thermal-dependent error and must be calibrated almost continuously. This article proposes a novel method of online (continuous) calibration of inertial sensors with the aid of the data from the GNSS receiver during the vehicle's route. We performed data fusion using an extended Kalman filter (EKF) and calibrated the input sensors through error backpropagation. The algorithm thus calibrates the INS sensors while the GNSS receiver signal is good, and after a GNSS failure, for example in tunnels, the position is predicted only by low-cost inertial sensors. Such an approach significantly improved the localization precision in comparison with offline calibrated inertial localization with the same sensors.
Audience	Academic
Author	Nemec, Dusan Andel, Jan Simak, Vojtech Kekelak, Juraj
AuthorAffiliation	Department of Control and Information Systems, Faculty of Electrical Engineering and Information Technology, University of Žilina, 010 26 Žilina, Slovakia; vojtech.simak@uniza.sk (V.S.); andel.jano@gmail.com (J.A.); juraj.kekelak@feit.uniza.sk (J.K.)
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Cites_doi	10.1109/CarpathianCC.2017.7970452 10.3390/s22218447 10.1109/RTSI.2015.7325144 10.1016/j.ifacol.2018.08.151 10.1109/TAES.2011.5751259 10.1088/0957-0233/23/10/105105 10.1109/INOCON50539.2020.9298316 10.3390/rs14030752 10.1109/TIV.2020.2980758 10.1016/j.trpro.2021.07.066 10.1016/j.inffus.2010.01.003 10.1109/WPNC.2014.6843306 10.1109/TAES.2002.1008998 10.1109/JSEN.2015.2459767 10.1109/DESEC.2017.8073847 10.1109/ICINFA.2010.5512070 10.1155/2010/967245 10.1109/JSEN.2016.2597292 10.1109/CEEE.2017.8412926 10.1109/IoT-SIU.2018.8519902 10.1109/JSTARS.2016.2546316 10.1016/j.inffus.2004.07.002 10.1109/ICDI3C.2018.00013 10.1109/ACCESS.2020.3006210 10.23919/ChiCC.2018.8483718 10.1016/j.inffus.2010.06.006 10.1016/j.dt.2019.08.011 10.3390/s17061324 10.1109/WiSPNET45539.2019.9032769
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Snippet	Global satellite navigation systems (GNSSs) are the most-used technology for the localization of vehicles in the outdoor environment, but in the case of a...
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SubjectTerms	Accelerometers Accuracy Algorithms Artificial intelligence Calibration Communication Communications equipment Electronics in navigation error backpropagation global satellite navigation Inertial navigation Inertial navigation (Aeronautics) inertial sensors Localization Microelectromechanical systems Neural networks online calibration Receivers & amplifiers Sensors Technology application Wireless access points
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Title	Online Calibration of Inertial Sensors Based on Error Backpropagation
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