TY - JOUR
T1 - Optimization of Virtual Reference Station Algorith Using Empirical Models of Variogram Function
TT - بهینه سازی الگوریتم ایستگاه مرجع مجازی ((VRSبا استفاده از مدل های تجربی تابع تغییرنما
JF - kntu-jgit
JO - kntu-jgit
VL - 5
IS - 2
UR - http://jgit.kntu.ac.ir/article-1-463-en.html
Y1 - 2017
SP - 1
EP - 18
KW - NRTK
KW - VRS
KW - Ordinary Kriging
KW - Variogram function
KW - Sagnac effect
N2 - 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.
M3 10.29252/jgit.5.2.1
ER -