scholarly journals A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China

Sensors ◽  
2017 ◽  
Vol 18 (2) ◽  
pp. 65 ◽  
Author(s):  
Yidong Lou ◽  
Jinfang Huang ◽  
Weixing Zhang ◽  
Hong Liang ◽  
Fu Zheng ◽  
...  
Keyword(s):  
Real Time ◽  
Tropospheric Delay ◽  
Zenith Tropospheric Delay

A new simplified zenith tropospheric delay model for real-time GNSS applications

GPS Solutions ◽  
2021 ◽  
Vol 25 (2) ◽  
Author(s):  
Jian Mao ◽  
Qiang Wang ◽  
Yubin Liang ◽  
Tiejun Cui
Keyword(s):  
Real Time ◽  
Tropospheric Delay ◽  
Delay Model ◽  
Zenith Tropospheric Delay

Assessment of EGNOS Model over Asia Area Using IGS_ZTD

2014 ◽  
Vol 501-504 ◽  
pp. 2182-2186
Author(s):  
Li Long Liu ◽  
Miao Zhou ◽  
Teng Xu Zhang ◽  
Wei Wang ◽  
Liang Ke Huang
Keyword(s):  
Real Time ◽  
Root Mean Square ◽  
Tropospheric Delay ◽  
Mean Square ◽  
Correction Model ◽  
The Real ◽  
Zenith Tropospheric Delay ◽  
Temporal And Spatial ◽  
Delay Correction

In this study, three years of the zenith tropospheric delay (ZTD) data observed from 46 International GNSS system (IGS) sites distributed in Asian area used to assess the effectiveness and accuracy of ZTD calculated from EGNOS model, and the application of the EGNOS model are also analyzed in Asian area. Relative to IGS observed ZTD, the bias and root mean square (RMS) for ZTD calculated from EGNOS model presents an obvious variation in temporal and spatial. These results provide a reference for the study of the tropospheric delay correction model, the real-time GNSS navigation and positioning.

scholarly journals Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS and ERA5 Data

2021 ◽  
Vol 13 (5) ◽  
pp. 1004
Author(s):  
Song Li ◽  
Tianhe Xu ◽  
Nan Jiang ◽  
Honglei Yang ◽  
Shuaimin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
High Efficiency ◽  
Rapid Development ◽  
Support Vector ◽  
Tropospheric Delay ◽  
Improvement Rate ◽  
Zenith Tropospheric Delay ◽  
Background Data ◽  
Mean Square Errors

The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predicting ZTD. In this paper, we develop three new regional ZTD models based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTD products and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, the ERA5 data is extended to ERA5S-ZTD and ERA5P-ZTD as the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to construct ZTD models based on the LSSVM algorithm, including the without background data, with the ERA5S-ZTD, and with the ERA5P-ZTD. To investigate the advantage and feasibility of the proposed ZTD models, we evaluate the accuracy of two background data and three schemes by segmental comparison with the IGS-ZTD of 85 IGS stations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for the ERA5S-ZTD, and 10.7 mm for the ERA5P-ZTD. The overall average RMSE is 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for the ZTD model with ERA5S-ZTD and ERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for the ZTD model with ERA5P-ZTD, the loop verification is performed by estimating the ZTD values of each available IGS station. In actuality, the monthly improvement rate of estimated ZTD is positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations.

An analysis of BDS-3 real-time PPP: Time transfer, positioning, and tropospheric delay retrieval

Measurement ◽  
2021 ◽  
Vol 172 ◽  
pp. 108871
Author(s):  
Yulong Ge ◽  
Shaoxin Chen ◽  
Tao Wu ◽  
Caoming Fan ◽  
Weijin Qin ◽  
...  
Keyword(s):  
Real Time ◽  
Tropospheric Delay ◽  
Time Transfer
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