Tropospheric Delay Prediction Based on Phase Space Reconstruction and Gaussian Process Regression

LUO Yiyong; ZHANG Jingying; CHEN Junyi; HUANG Cheng; WANG Xin

doi:10.13203/j.whugis20190018

Volume 46 Issue 1

Jan. 2021

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Geomatics and Information Science of Wuhan University > 2021 > 46(1): 103-110. > DOI: 10.13203/j.whugis20190018

LUO Yiyong, ZHANG Jingying, CHEN Junyi, HUANG Cheng, WANG Xin. Tropospheric Delay Prediction Based on Phase Space Reconstruction and Gaussian Process Regression[J]. Geomatics and Information Science of Wuhan University, 2021, 46(1): 103-110. DOI: 10.13203/j.whugis20190018

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Tropospheric Delay Prediction Based on Phase Space Reconstruction and Gaussian Process Regression

1.
Faculty of Geomatics, East China University of Technology, Nanchang 330013, China
2.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
3.
School of Business, Ningbo University, Ningbo 315211, China

Funds:

The National Natural Science Foundation of China 41861058

The National Natural Science Foundation of China 41664001

the Natural Science Foundation of Jiangxi Province 20202BABL204070

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Author Bio:
LUO Yiyong, PhD, associate professor, specializes in deformation data processing method.E-mail: luoyiyong@whu.edu.cn
Received Date: December 11, 2019
Published Date: January 04, 2021

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Abstract

Abstract

Zenith tropospheric delay (ZTD) is a key factor affecting global positioning system (GPS) positioning accuracy. In order to improve the prediction accuracy of ZTD, a Gaussian process(GP) regression prediction model based on phase space reconstruction is proposed.In view of the chaotic characteristics of ZTD time series, using the ZTD data provided by the International Global Navigation Satellite System Service (IGS) stations.Firstly, the embedded dimension is determined using Cao method, phase space reconstruction of ZTD data is carried out, and the precision and accuracy of ZTD using GP model for 12 IGS ststions at different latitude levels in the southern and northern hemisphere are explored.Then, in order to verify the effectiveness of GP model, the prediction results are compared with the original data and prediction results of the back propagation (BP) neural network model, and the influence of different time on the prediction accuracy of ZTD is further explored. Finally, the influence of longitude and altitude on the prediction accuracy of ZTD is analyzed.The results show that the root mean square error (RMSE) of GP model prediction results reaches mm level, the correlation between GP model and theoretical value reaches 0.997, and the prediction accuracy index is obviously better than that of BP neural network model. The prediction accuracy of GP model in the southern hemisphere is higher than that in the northern hemisphere, and RMSE in the high latitude area is less than 3.6 mm, which is more suitable for the tropospheric delay prediction in the high latitude area. In the time domain of the study, the prediction accuracy of GP model at night is higher than that in the day at most sites, the longitude has no obvious influence on the prediction accuracy of ZTD, and the altitude is proportional to the prediction accuracy of ZTD. Therefore, GP model has better practicability and feasibility for the prediction of tropospheric delay.
- tropospheric delay,
- phase space reconstruction,
- Gaussian process model,
- prediction accuracy

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