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A novel hybrid observation prediction methodology for bridging GNSS outages in INS/GNSS systems

Published online by Cambridge University Press:  23 May 2022

Linzhouting Chen Open the ORCID record for Linzhouting Chen [Opens in a new window] ,
Zhanchao Liu Open the ORCID record for Zhanchao Liu [Opens in a new window]  and
Jiancheng Fang
Linzhouting Chen*
Affiliation:
College of Aerospace Engineering, Guizhou Institute of Technology, Guiyang, China
Zhanchao Liu
Affiliation:
Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China
Jiancheng Fang
Affiliation:
Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China
*
*Corresponding author. E-mail: chenlzt@163.com
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Abstract

The integration of the inertial navigation system (INS) and global navigation satellite system (GNSS) is suited for localisation and navigation applications, such as aircrafts, land vehicles and ships. The primary challenge is for navigation system to achieve accurate and reliable navigation solution during GNSS outages. This paper presents an observation prediction methodology for INS/GNSS bridging GNSS outages, which combines partial least squares regression (PLSR) and Gaussian process regression (GPR) to model the INS/GNSS observations and enable a Kalman filter to estimate INS errors. The performance of proposed PLSR/GPR prediction methodology was validated through four GNSS outages taken on flight experiment data, including diverse manoeuvre conditions. The experiment results demonstrate that remarkable performance enhancements are achieved through applying the proposed PLSR/GPR prediction methodology into INS/GNSS integration.

Keywords

INS/GNSSGNSS outagesobservation predictionhybrid PLSR/GPR modelHP filter
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 5 , September 2022 , pp. 1206 - 1225
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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