Bulletin of Surveying and Mapping ›› 2023, Vol. 0 ›› Issue (9): 113-116,143.doi: 10.13474/j.cnki.11-2246.2023.0274

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Accurcy analysis of GNSS height transformation based on deep BP/ELMAN neural network in the mountains

WEI Dehong1, XUAN Jianhao2, YANG Jiawei3, ZHANG Xingfu1, YU Xu1   

  1. 1. Surving and Mapping Engineering Department, Guangdong University of Technology, Guangzhou 510006, China;
    2. College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China;
    3. Linyi Natural Resources and Planning Bureau, Linyi 276000, China
  • Received:2022-12-15 Published:2023-10-08

Abstract: It is of great practical value to transform the GNSS geodetic height into the normal height with higher precision in the mountains. This paper uses the high-precision GNSS leveling data of the GSVS2017 project to analyze the accuracy of GNSS height transformation based on deep BP/ELMAN neural network, general regression neural network (GRNN), radial basis function neural network (RBFNN), support vector machine regression (SVR), quadratic curve fitting and surface fitting, etc. The results show that: ①When the distance between training points is 50, 30, 15, 10 and 5 km, the deep BP/ELMAN neural network with the hidden layer activation function ReLU can obtain higher precision results, and they are more accurate than GRNN, RBFNN, SVR, quadratic curve fitting and surface fitting. ②The deep BP/ELMAN neural network with the hidden layer activation function ReLU are used for GNSS height transformation. Among the five kinds of training point spacing, more than 90% of the height difference can meet the fourth-order leveling accuracy, and more than 75% of height difference can meet the third-order leveling accuracy; when the distance between training points spacing is 5 km, more than 55% of the height difference can meet the second-order leveling accuracy.

Key words: deep learning, neural network, GNSS height transformation, accuracy analysis

CLC Number: