Variance-covariance estimation of GPS Networks

Summary It is quite easy to estimate the variance-covariance (VCV) matrix for single session surveys or local networks, but difficult where these local networks are combined together to form a regional network. Our main aim is to develop an appropriate VCV model to combine all the different types of networks, either global, regional or local. By careful estimation and combination of the individual VCVs of the local networks, we can form a unique VCV for local, regional and global networks. Different techniques are used to derive appropriate models for the variancecovariance components of the Global Positioning System (GPS) networks. The VCV models were estimated using homogeneous and heterogeneous data. The variance-covariance components are empirically derived using (a) the covariance of the observations of homogeneous data, (b) a combination of the covariance of the observationsP^–1 and the covariance of the signal componentsC_ss (for either homogeneous and/or heterogeneous data), (c) only the variances are used to determine the variancecovariance, their covariances being zeros. We compare the solutions of the VCV developed for homogeneous and/or heterogeneous data with other developed VCVs. It was observed that the derived VCV model for the combined homogeneous and/or heterogeneous data of case (b) gives the best estimates in all cases.