Electronics and Electrical Engineering and Control

Factor graph optimization based multi-GNSS positioning with robust variance component estimation

  • Chuang SHI ,
  • Zhixin WANG ,
  • Hao ZHANG ,
  • Tuan LI ,
  • Zhipeng WANG
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  • 1.School of Electronics and Information Engineering,Beihang University,Beijing 100191,China
    2.Laboratory of Navigation and Communication Fusion Technology,Ministry of Industry and Information Technology,Beijing 100191,China
    3.State Key Laboratory of CNS/ATM,Beijing 100191,China
    4.Advanced Research Institute of Multidisciplinary Sciences,Beijing Institute of Technology,Beijing 100081,China
E-mail: tuanli@buaa.edu.cn

Received date: 2024-12-06

  Revised date: 2024-12-10

  Accepted date: 2024-12-24

  Online published: 2024-12-30

Supported by

National Key Research and Development Program of China(2023YFB3907302);Beijing Natural Science Foundation(4254104);Open Project Program of State Key Laboratory of Communication Navigation, Surveillance/Air Traffic Management(2024B21);BIT Research and Innovation Promoting Project(2024YCXY029)

Abstract

With the development of Global Navigation Satellite System (GNSS), multi-GNSS positioning has been shown to provide a more effective solution for accurate localization, benefiting from an increased number of available satellites and improved geo-metric distribution. Recent research indicates that Factor Graph Optimization (FGO) based multi-GNSS positioning outperforms traditional algorithms. However, the refinement of the stochastic model and the adaptive weighting of FGO-based multi-GNSS positioning remain underexplored. We propose a robust Helmert Variance Component Estimation (HVCE) method to further enhance the performance of multi-GNSS positioning in challenging urban scenarios by adaptive weighting for multi-GNSS. Additionally, the robust algorithm IGG-III is applied to improve both the robustness of the HVCE method and the state estimation within the FGO framework. The results of vehicle-borne tests show that compared with the single FGO scheme, the proposed algorithm improves positioning accuracy by 35.4%, 8.7%, and 25.1% in the north, east, and vertical directions, respectively. Overall, the proposed algorithm is validated to be an effective approach to improving multi-GNSS performance in complex urban environments by refining the stochastic model and adaptively weighting the measurements.

Cite this article

Chuang SHI , Zhixin WANG , Hao ZHANG , Tuan LI , Zhipeng WANG . Factor graph optimization based multi-GNSS positioning with robust variance component estimation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(6) : 531623 -531623 . DOI: 10.7527/S1000-6893.2024.31623

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