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Improvement of Multi-GNSS Precise Point Positioning Performances with Real Meteorological Data

Published online by Cambridge University Press:  12 July 2018

Ke Su  and
Shuanggen Jin
Ke Su
Affiliation:
(Shanghai Key Laboratory of Space Navigation and Positioning Technology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China) (University of Chinese Academy of Sciences, Beijing 100049, China)
Shuanggen Jin*
Affiliation:
(Shanghai Key Laboratory of Space Navigation and Positioning Technology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China) (School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)
*
(E-mail: [email protected])
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Abstract

Tropospheric delay is one of the main error sources in Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP). Zenith Hydrostatic Delay (ZHD) accounts for 90% of the total delay. This research focuses on the improvements of ZHD from tropospheric models and real meteorological data on the PPP solution. Multi-GNSS PPP experiments are conducted using the datasets collected at Multi-GNSS Experiments (MGEX) network stations. The results show that the positioning accuracy of different GNSS PPP solutions using the meteorological data for ZHD correction can achieve an accuracy level of several millimetres. The average convergence time of a PPP solution for the BeiDou System (BDS), the Global Positioning System (GPS), Global Navigation Satellite System of Russia (GLONASS), BDS+GPS, and BDS+GPS+GLONASS+Galileo are 55·89 min, 25·88 min, 33·30 min, 20·50 min and 15·71 min, respectively. The results also show that atmospheric parameters provided by real meteorological data have little effect on the horizontal components of positioning compared to the meteorological model, while in the vertical component, the positioning accuracy is improved by 90·6%, 33·0%, 22·2% and 19·8% compared with the standard atmospheric model, University of New Brunswick (UNB3m) model, Global Pressure and Temperature (GPT) model, and Global Pressure and Temperature-2 (GPT2) model and the convergence times are decreased 51·2%, 32·8%, 32·5%, and 32·3%, respectively.

Keywords

GNSSPPPTropospheric delayMeteorology
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 6 , November 2018 , pp. 1363 - 1380
Copyright
Copyright © The Royal Institute of Navigation 2018 

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