Abstract
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.
Highlights
Data processing in geodetic network often count on the least squares adjustment (LSA) method, an appropriate stochastic model of the observables is constantly required (Amiri-Simkooei, 2007)
The estimated variance component of the cadastral network using least squares variance component estimation (LS-VCE) method has been presented in this paper
Cadastral network utilized the observation data from several types of instrument used such as chain measurement, Electronic Distance Measurement (EDM) and total station
Summary
Data processing in geodetic network often count on the least squares adjustment (LSA) method, an appropriate stochastic model of the observables is constantly required (Amiri-Simkooei, 2007). The geodetic community is actively seeking an alternative method for the determination of VCE To address this challenge, Amiri-Simkooei (2007) come out with a new method called LS-VCE, where using the principle of least square and weight matrix is defined by the user. Information available on the covariance matrix of the observation is crucial This is because every measurement data or observation data of geodetic network are necessary to know their level of accuracy in the form of a standard deviation or variance. It allows us to investigate the various contributing error factors in the observations (Amiri-Simkooei, 2007) Such an example, what if a set of measurement data is computed by using StarNet software have failed the Global test (Chi-Square test). Example of a set of measurement data have failed Chi-Square test in StarNet software
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