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Improved fault detection and exclusion method based on generalised least squares model for RTK positioning

Published online by Cambridge University Press:  12 August 2025

Yingying Jiang Open the ORCID record for Yingying Jiang [Opens in a new window] ,
Shuguo Pan Open the ORCID record for Shuguo Pan [Opens in a new window] ,
Min Zhang ,
Huizhen Yu ,
Wang Gao ,
Qian Meng  and
Hao Wang
Yingying Jiang
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing, China Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Southeast University, Nanjing, China
Shuguo Pan*
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing, China Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Southeast University, Nanjing, China State Key Laboratory of Smart Grid Protection and Control, Nari Group Corporation, Nanjing, China
Min Zhang
Affiliation:
State Key Laboratory of Smart Grid Protection and Control, Nari Group Corporation, Nanjing, China
Huizhen Yu
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing, China Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Southeast University, Nanjing, China
Wang Gao
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing, China Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Southeast University, Nanjing, China
Qian Meng
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing, China Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Southeast University, Nanjing, China
Hao Wang
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing, China Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Southeast University, Nanjing, China
*
Corresponding author: Shuguo Pan; Email: psg@seu.edu.cn
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Abstract

With increased global navigation satellite system (GNSS) signals and degraded observation environments, the correctness of ambiguity resolution is disturbed, causing unexpected real-time kinematic (RTK) positioning solutions. This paper presents an improved fault detection and exclusion (FDE) method based on the generalized least squares (GLS) model. The correlated GLS model is constructed by regarding double-differencing (DD) integer ambiguities as the known parameters. Meanwhile, the validity of residuals as crucial components of fault detection could be enhanced by the iterative re-weighted least squares (IRLS) method rather than the least squares (LS) without robustness. A static test with artificial faults and a dynamic test with natural faults were carried out, respectively. By analyzing test statistics of the enhanced FDE algorithm and comparing its positioning errors with those from the classical LS, it is shown that our method can provide high-precision and high-reliability RTK solutions facing wrong DD fixed ambiguities due to observation faults.

Keywords

fault detection and exclusion (FDE)fixed ambiguitiesgeneralised least squares (GLS)iterative re-weighted least squares (IRLS)reliability

Information

Type
Research Article
Information
The Journal of Navigation , First View , pp. 1 - 22
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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