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Concepts of network and deformation analysis
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The article was published on 1987-01-01 and is currently open access. It has received 240 citations till now. The article focuses on the topics: Deformation (meteorology).read more
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Estimating regional deformation from a combination of space and terrestrial geodetic data
TL;DR: In this article, an approach for efficiently combining different types of geodetic data to estimate time-dependent motions of stations in a region of active deformation is discussed. But the work is limited to the case of finite constraints and stochastic perturbation of parameters.
Journal ArticleDOI
Least-squares variance component estimation
TL;DR: In this paper, the LS-VCE method is described for three classes of weight matrices: a general weight matrix, a weight matrix derived from the class of elliptically contoured distributions.
Journal ArticleDOI
A discrimination test procedure for ambiguity resolution on-the-fly
TL;DR: In this paper, an ambiguity discrimination test procedure based on a test statistic which is constructed by the difference (not the ratio as used in current procedures) between the minimum and second minimum quadratic form of the residuals in ambiguity identification, and its standard deviation, is proposed.
Journal ArticleDOI
Land Subsidence of Jakarta (Indonesia) and its Geodetic Monitoring System
Hasanuddin Z. Abidin,Rochman Djaja,Dudy Darmawan,Samsul Hadi,Arifin Akbar,H. Rajiyowiryono,Y. Sudibyo,Irwan Meilano,M. A. Kasuma,Joenil Kahar,C. Subarya +10 more
TL;DR: In this article, the authors investigated the characteristics and pattern of land subsidence in the Jakarta area using data from three repeated leveling surveys performed in 1982, 1991, and 1997, and two repeated GPS surveys conducted in 1997 and 1999.
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Least-squares variance component estimation: theory and GPS applications
TL;DR: In this paper, the LS-VCE method is used to estimate the covariance matrix of the (co) variance component, and the minimum variance estimator of the variance component is obtained by choosing the weight matrix as the inverse of the covariation matrix.