| dc.creator (作者) | 林老生 | zh_TW |
| dc.creator (作者) | Lin, Lao-Sheng | - |
| dc.date (日期) | 2007-05 | en_US |
| dc.date.accessioned | 18-Nov-2008 09:03:43 (UTC+8) | - |
| dc.date.available | 18-Nov-2008 09:03:43 (UTC+8) | - |
| dc.date.issued (上傳時間) | 18-Nov-2008 09:03:43 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/8575 | - |
| dc.description.abstract (摘要) | 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. | - |
| dc.format | application/ | en_US |
| dc.language | en | en_US |
| dc.language | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Journal of Surveying Engineering, 133(2), 81-89 | en_US |
| dc.subject (關鍵詞) | Geoid; Global positioning; Height; Neural networks; Surveys | - |
| dc.title (題名) | Application of a Back-Propagation Artificial Neural Network to Regional Grid-Based Geoid Model Generation Using GPS and Leveling Data | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1061/(ASCE)0733-9453(2007)133:2(81) | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1061/(ASCE)0733-9453(2007)133:2(81) | en_US |
