scholarly journals CRITERION FOR COMPARING THE VARIANCE COMPONENT ESTIMATION METHODS USED FOR ADJUSTING HORIZONTAL CONTROL NETWORKS

2012 ◽  
Vol 32 (1) ◽  
pp. 6-13
Author(s):  
Erol Yavuz ◽  
Orhan Baykal
Keyword(s):  
Stochastic Model ◽  
Variance Component ◽  
Statistical Tests ◽  
Estimation Methods ◽  
Variance Component Estimation ◽  
Control Networks ◽  
Component Estimation ◽  
Using Data ◽  
Horizontal Control

Determination of which stochastic model taken in hand for this study is suitable under conditions, when comparing stochastic models, used for adjusting horizontal control networks, is the aim of this study. Some well-known variance component estimation methods like Conventional, Helmert, MTNQUE, AUE, and Förstner, which have been developed to determine the stochastic model, necessary to be formed in a real way for adjusting geodetic nets, have been compared. For comparing the models mentioned above, concrete deciding criteria, using statistical tests, have been defined and the determination of which model is superior has been studied. For comparison of the models, numerical experiment using data, which belong to the part of Istanbul Metropolitan Triangulation Network (Asiatic side of Istanbul), has been performed.

GPS-BDS-Galileo double-differenced stochastic model refinement based on least-squares variance component estimation

2021 ◽  
pp. 1-16
Author(s):  
Hong Hu ◽  
Xuefeng Xie ◽  
Jingxiang Gao ◽  
Shuanggen Jin ◽  
Peng Jiang
Keyword(s):  
Stochastic Model ◽  
Least Squares ◽  
Stochastic Models ◽  
Variance Component ◽  
Satellite System ◽  
Variance Component Estimation ◽  
Short Baseline ◽  
Component Estimation ◽  
Zero Baseline ◽  
Global Navigation Satellite

Abstract Stochastic models are essential for precise navigation and positioning of the global navigation satellite system (GNSS). A stochastic model can influence the resolution of ambiguity, which is a key step in GNSS positioning. Most of the existing multi-GNSS stochastic models are based on the GPS empirical model, while differences in the precision of observations among different systems are not considered. In this paper, three refined stochastic models, namely the variance components between systems (RSM1), the variances of different types of observations (RSM2) and the variances of observations for each satellite (RSM3) are proposed based on the least-squares variance component estimation (LS-VCE). Zero-baseline and short-baseline GNSS experimental data were used to verify the proposed three refined stochastic models. The results show that, compared with the traditional elevation-dependent model (EDM), though the proposed models do not significantly improve the ambiguity resolution success rate, the positioning precision of the three proposed models has been improved. RSM3, which is more realistic for the data itself, performs the best, and the precision at elevation mask angles 20°, 30°, 40°, 50° can be improved by 4⋅6%, 7⋅6%, 13⋅2%, 73⋅0% for L1-B1-E1 and 1⋅1%, 4⋅8%, 16⋅3%, 64⋅5% for L2-B2-E5a, respectively.

scholarly journals ADJUSTMENT OF CADASTRAL NETWORK USING LEAST-SQUARES VARIANCE COMPONENT ESTIMATION

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 161-167
Author(s):  
N. K. Bidi ◽  
A. H. M. Din ◽  
Z. A. M. Som ◽  
A. H. Omar
Keyword(s):  
Stochastic Model ◽  
Least Squares ◽  
Variance Components ◽  
Variance Component ◽  
Geodetic Network ◽  
Observation Data ◽  
Variance Component Estimation ◽  
Station Coordinates ◽  
Component Estimation ◽  
Estimation Of Variance

Abstract. The role of the stochastic model very important in data processing of geodetic network since it describes the accuracy of the measurements and their correlation with each other. Knowledge of weights of the observables is required to provide a better understanding of the sources of errors and to model the error, hence the weights need to be determined correctly. This study concentrates on the estimation of variance components from different types of instruments used in the cadastral survey. The ideas are to combine the conventional and advanced instruments in a traverse network to enhance the estimated variance component in the stochastic model. Thus, Least Squares Variance Component Estimation (LS-VCE) method was used in this study because the method is simple, flexible and attractive due to the precision of variance estimators that can be directly obtained. Observation data come with several types of instruments such as chain measurement, Electronic Distance Measurement and total station were utilized. The findings showed that LS-VCE method was very reliable in cadastral network application. Besides, the results revealed that the estimated variance components for distance scale error, σp seem to become unrealistic for each data tested. It was found that the traverse network which included chain survey, showed the insignificant result to the precision of station coordinates when the measurements were combined.

scholarly journals An overview of variance component estimation

Metrika ◽  
1995 ◽  
Vol 42 (1) ◽  
pp. 215-230 ◽  
Author(s):  
Shayle R. Searle
Keyword(s):  
Variance Component ◽  
Variance Component Estimation ◽  
Component Estimation
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