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Joint Multi-Point GNSS Positioning Method Based on Neural Network-Gauss-Helmert Model
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Haifei LIN1, Youzhi PENG1, *, Yuguo XIA2, Haopeng HE1
Journal of Geodesy and Geodynamics | 2025, 45(3) : 303 - 307
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Journal of Geodesy and Geodynamics | 2025, 45(3): 303-307
Joint Multi-Point GNSS Positioning Method Based on Neural Network-Gauss-Helmert Model
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Haifei LIN1, Youzhi PENG1, *, Yuguo XIA2, Haopeng HE1
Affiliations
  • 1 Key Laboratory of Earthquake Geodesy, CEA, 40 Hongshance Road, Wuhan 430071, China
  • 2 Wuhan Seismic Metrological Verification and Surveying Engineering Institute Co Ltd, 40 Hongshance Road, Wuhan 430071, China
  • LIN Haifei, postgraduate, majors in precise GNSS data processing, E-mail:

About Author:

LIN Haifei, postgraduate, majors in precise GNSS data processing, E-mail:

Published: 2025-03-15 doi: 10.14075/j.jgg.2024.04.191
Outline
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To reduce the positioning errors of global navigation satellite system(GNSS) in complex environments, we propose a method that combines high-precision points with distance intersection to accurately estimate the coordinates of an undetermined point. The observation equation is constructed as a nonlinear Gauss-Helmert model. To address the nonlinearity within this model, we introduce a back-propagation(BP) neural network for auxiliary processing. Compared with the traditional linearization methods, the BP neural network can effectively fit complex nonlinear functional relationships. The simulation experiments and actual measurement results show that this method can significantly reduce the impact of complex environments on positioning accuracy, and improve the positioning accuracy in E, N and U directions by 78.1%, 72.8%, and 79.2%, respectively.

GNSS  /  complex environments  /  Gauss-Helmert model  /  back-propagation neural network  /  error estimation
林海飞, 彭友志, 夏玉国, 何浩鹏. 基于神经网络-高斯赫尔默特模型联合多点GNSS定位方法. 大地测量与地球动力学, 2025 , 45 (3) : 303 -307 . DOI: 10.14075/j.jgg.2024.04.191
Haifei LIN, Youzhi PENG, Yuguo XIA, Haopeng HE. Joint Multi-Point GNSS Positioning Method Based on Neural Network-Gauss-Helmert Model[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (3) : 303 -307 . DOI: 10.14075/j.jgg.2024.04.191
  • Natural Science Foundation of Hubei Province(2021CFB515)
Year 2025 volume 45 Issue 3
22
7
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Article Info
doi: 10.14075/j.jgg.2024.04.191
  • Receive Date:2024-04-25
  • Online Date:2026-10-30
  • Published:2025-03-15
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History
  • Received:2024-04-25
Funding
Natural Science Foundation of Hubei Province(2021CFB515)
Affiliations
    1 Key Laboratory of Earthquake Geodesy, CEA, 40 Hongshance Road, Wuhan 430071, China
    2 Wuhan Seismic Metrological Verification and Surveying Engineering Institute Co Ltd, 40 Hongshance Road, Wuhan 430071, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
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Percentage of
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Genus
种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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