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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/8575


    Title: Application of a Back-Propagation Artificial Neural Network to Regional Grid-Based Geoid Model Generation Using GPS and Leveling Data
    Authors: 林老生
    Lin, Lao-Sheng
    Keywords: Geoid;Global positioning;Height;Neural networks;Surveys
    Date: 2007-05
    Issue Date: 2008-11-18 09:03:43 (UTC+8)
    Abstract: The height difference between the ellipsoidal height h and the orthometric height H is called undulation N. The key issue in transforming the global positioning system (GPS)-derived ellipsoidal height to the orthometric height is to determine the undulation value accurately. If the undulation N for a point whose position is determined by a GPS receiver can be estimated in the field, then the GPS-derived three-dimensional geocentric coordinate in WGS-84 can be transformed into a local coordinate system and the orthometric height in real-time. In this paper, algorithms of applying a back-propagation artificial neural network (BP ANN) to develop a regional grid-based geoid model using GPS data (e.g., ellipsoidal height) and geodetic leveling data (e.g., orthometric height) are proposed. In brief, the proposed algorithms include the following steps: (1) establish the functional relationship between the point’s plane coordinates and its undulation using the BP ANN according to the measured GPS data and leveling data; (2) develop a regional grid-based geoid model using the imaginary grid plane coordinates with a fixed grid interval and the trained BP ANN; (3) develop an undulation interpolation algorithm to estimate a specific point’s undulation using the generated grid-based geoid model; and (4) estimate the point’s undulation in the field and transform the GPS ellipsoidal height into the orthometric height in real-time. Three data sets from the Taiwan region are used to test the proposed algorithms. The test results show that the undulation interpolation estimation accuracy using the generated grid-based geoid is in the order of 2 — 4 cm. The proposed algorithms and the detailed test results are presented in this paper.
    Relation: Journal of Surveying Engineering, 133(2), 81-89
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1061/(ASCE)0733-9453(2007)133:2(81)
    DOI: 10.1061/(ASCE)0733-9453(2007)133:2(81)
    Appears in Collections:[地政學系] 期刊論文

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