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Engineering >> 2020, Volume 6, Issue 8 doi: 10.1016/j.eng.2020.08.004

Realization of an Optimal Dynamic Geodetic Reference Frame in China: Methodology and Applications

a Chinese Academy of Surveying and Mapping, Beijing 100036, China
b Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
c School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

Received:2018-12-15 Revised:2020-06-11 Accepted: 2020-06-23 Available online:2020-08-13

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The China Geodetic Coordinate System 2000 (CGCS2000) has been used for several years as a formal published reference frame. The coordinates of all global navigation satellite system (GNSS) stations in China need to be corrected to align with the CGCS2000 frame. Different strategies can be adopted for the realization of an optimal reference frame. However, different strategies lead to different results, with differences as great as several decimeters when GNSS station coordinates are transformed into CGCS2000-defined coordinates. The two common methods for the coordinate correction of a GNSS station are quasi-stable adjustment under CGCS2000 and plate movement correction, and the differences between their results can be greater than 10 cm. In this study, a statistic method called “supervised clustering”is applied to the selection of GNSS reference stations; a new scheme named “artition spacing” for the grouping of all processed GNSS stations is proposed; and the plate movement correction method is used to correct the coordinates of all GNSS stations from the GNSS epoch to the CGCS2000 epoch. The results from the new partitioning method were found to be significantly better than those from the conventional station-blocking approach. When coordinates from the stations without grouping were used as the standard, the accuracy of all the three-dimensional coordinate components from the new partitioning method was better than 2 mm. The root mean squares (RMSs) of the velocities in the x, y, and z directions resulting from the supervised clustering method were 0.19, 0.45, and 0.32 mm∙a1, respectively, which were much smaller than the values of 0.92, 0.72, and 0.97 mm∙a1 that resulted from the conventional approach. In addition, singular spectrum analysis (SSA) was used to model and predict the position nonlinear movements. The modeling accuracies of SSA were better than 3, 2, and 5 mm in the east (E), north (N), and up (U) directions, respectively; and its prediction accuracies were better than 5 mm and 1 cm for the horizontal and vertical domains, respectively.


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