Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/74471
DC Field | Value | Language |
---|---|---|
dc.contributor | 地政學系 | |
dc.creator | Lin, Lao-Sheng;Teng, H.-C. | |
dc.creator | 林老生 | zh_TW |
dc.date | 2012 | |
dc.date.accessioned | 2015-04-10T08:34:36Z | - |
dc.date.available | 2015-04-10T08:34:36Z | - |
dc.date.issued | 2015-04-10T08:34:36Z | - |
dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/74471 | - |
dc.description.abstract | e-GPS (Electronic Global Positioning System), which is used in Taiwan, is a type of real-time kinematic satellite positioning technology, such as VRS-RTK (Virtual Reference Station Real Time Kinematic). The height difference between ellipsoidal height h and orthometric height H is called undulation N. For a point p, if the values of h from e-GPS and N from regional geoid model are known, the H value of point p can be calculated using the following equation: H = h-N. This is the basic principle of e-GPS leveling. Data analysis of test results revealed that the estimated orthometric height from e-GPS leveling may have systematic errors. This paper proposes a BP (back-propagation) neural network and BP neural network method (BP&BP) method to manage the systematic errors of the estimated orthometric height Ĥ from e-GPS leveling. The main goal of the proposed method is to mitigate the systematic errors of orthometric height H from e-GPS leveling efficiently. Subsequently, the e-GPS leveling accuracy may be improved. Three data sets (including plane coordinates, ellipsoidal height h from static GPS, orthometric height H from first-order leveling, and ellipsoidal height h from e-GPS) of 145 benchmarks from Tainan City, Taiwan, were used to test the proposed method. The test results show that the proposed method can mitigate the systematic errors of orthometric height H from e-GPS leveling efficiently. The proposed methods and the detailed test results are presented in this paper. | |
dc.format.extent | 176 bytes | - |
dc.format.mimetype | text/html | - |
dc.relation | 33rd Asian Conference on Remote Sensing 2012, ACRS 2012 | |
dc.subject | BP neural networks; Ellipsoidal Height; Neural network method; Orthometric heights; Real-time kinematic; Satellite positioning; Undulation; Virtual reference stations; Kinematics; Remote sensing; Neural networks | |
dc.title | Mitigating the systematic errors of E-GPS leveling using neural network method | |
dc.type | conference | en |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | conference | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | 會議論文 |
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