[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Contact us::
Site Facilities::
Search in website

Advanced Search
Receive site information
Enter your Email in the following box to receive the site news and information.
:: Volume 5, Issue 2 (9-2017) ::
jgit 2017, 5(2): 1-18 Back to browse issues page
Optimization of Virtual Reference Station Algorith Using Empirical Models of Variogram Function
Jamal Asgari * , Ardalan Malekzade , Alireza Amiri Simkooei
University of Isfahan
Abstract:   (4147 Views)
The Network Real Time Kinematic (NRTK) algorithm has been developed to overcome the traditional Real Time Kinematic (RTK) problems and limitations. This paper introduces an algorithm for Virtual Reference Station (VRS) generation and it investigates the accuracy of the corrections interpolation. After long baseline processing, ionospheric and tropospheric residuals are estimated for each baseline. Then, two methods of linear interpolation and ordinary Kriging are implemented. Ionospheric and tropospheric double differences biases are interpolated for an arbitrary direction. Single difference and zero difference VRS algorithms have been used. In the classical algorithm, corrections are applied to the single difference observations, but in the second algorithm, corrections are applied to the zero differenced VRS observations. The results of two algorithms have been compared with linear and ordinary Kriging interpolation method. The performance of the zero differenced VRS algorithm was better than that of the single difference. Also, ordinary Kriging method’s performance is better than linear interpolation method. Ordinary Kriging based on variogram function is then used to increase the accuracy of the corrections interpolation. To calculate variogram function, three empirical models, including spherical, exponential and Gaussian models have been used. After some statistical analysis, the Gaussian model has been chosen as the best empirical one. Interpolated corrections of the Gaussian model are used to the VRS algorithm. The results demonstrate that using variogram function instead of simple distance based covariance function leads to 50, 73 and 24% improvement in the accuracy of the north, east and up components.
Keywords: NRTK, VRS, Ordinary Kriging, Variogram function, Sagnac effect
Full-Text [PDF 1099 kb]   (1233 Downloads)    
Type of Study: Research | Subject: Geodesy
Received: 2017/10/7 | Accepted: 2017/10/7 | Published: 2017/10/7
Send email to the article author

XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Asgari J, Malekzade A, Amiri Simkooei A. Optimization of Virtual Reference Station Algorith Using Empirical Models of Variogram Function . jgit 2017; 5 (2) :1-18
URL: http://jgit.kntu.ac.ir/article-1-463-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 5, Issue 2 (9-2017) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
Persian site map - English site map - Created in 0.04 seconds with 36 queries by YEKTAWEB 4645