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GPS-BDS-Galileo double-differenced stochastic model refinement based on least-squares variance component estimation

Published online by Cambridge University Press:  09 July 2021

Hong Hu ,
Xuefeng Xie ,
Jingxiang Gao ,
Shuanggen Jin Open the ORCID record for Shuanggen Jin [Opens in a new window]  and
Peng Jiang
Hong Hu
Affiliation:
School of Resources and Environmental Engineering, Anhui University, Hefei, China.
Xuefeng Xie*
Affiliation:
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China. MNR Key Laboratory of Land Environment and Disaster Monitoring, Xuzhou, China.
Jingxiang Gao
Affiliation:
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China.
Shuanggen Jin
Affiliation:
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, China. Shanghai Astronomical Observatory, Chinese Academy of Science, Shanghai, China
Peng Jiang
Affiliation:
School of Resources and Environmental Engineering, Anhui University, Hefei, China.
*
*Corresponding author. E-mail: xfxiestyle@sina.com.
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Abstract

Stochastic models are essential for precise navigation and positioning of the global navigation satellite system (GNSS). A stochastic model can influence the resolution of ambiguity, which is a key step in GNSS positioning. Most of the existing multi-GNSS stochastic models are based on the GPS empirical model, while differences in the precision of observations among different systems are not considered. In this paper, three refined stochastic models, namely the variance components between systems (RSM1), the variances of different types of observations (RSM2) and the variances of observations for each satellite (RSM3) are proposed based on the least-squares variance component estimation (LS-VCE). Zero-baseline and short-baseline GNSS experimental data were used to verify the proposed three refined stochastic models. The results show that, compared with the traditional elevation-dependent model (EDM), though the proposed models do not significantly improve the ambiguity resolution success rate, the positioning precision of the three proposed models has been improved. RSM3, which is more realistic for the data itself, performs the best, and the precision at elevation mask angles 20°, 30°, 40°, 50° can be improved by 4⋅6%, 7⋅6%, 13⋅2%, 73⋅0% for L1-B1-E1 and 1⋅1%, 4⋅8%, 16⋅3%, 64⋅5% for L2-B2-E5a, respectively.

Keywords

GPS-BDS-GalileoLS-VCEstochastic modelinteger ambiguity resolutionpositioning precision
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 6 , November 2021 , pp. 1381 - 1396
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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