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題名 Mitigating the systematic errors of E-GPS leveling using neural network method
作者 Lin, Lao-Sheng;Teng, H.-C.
林老生
貢獻者 地政學系
關鍵詞 BP neural networks; Ellipsoidal Height; Neural network method; Orthometric heights; Real-time kinematic; Satellite positioning; Undulation; Virtual reference stations; Kinematics; Remote sensing; Neural networks
日期 2012
上傳時間 10-Apr-2015 16:34:36 (UTC+8)
摘要 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.
關聯 33rd Asian Conference on Remote Sensing 2012, ACRS 2012
資料類型 conference
dc.contributor 地政學系
dc.creator (作者) Lin, Lao-Sheng;Teng, H.-C.
dc.creator (作者) 林老生zh_TW
dc.date (日期) 2012
dc.date.accessioned 10-Apr-2015 16:34:36 (UTC+8)-
dc.date.available 10-Apr-2015 16:34:36 (UTC+8)-
dc.date.issued (上傳時間) 10-Apr-2015 16:34:36 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/74471-
dc.description.abstract (摘要) 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 (關聯) 33rd Asian Conference on Remote Sensing 2012, ACRS 2012
dc.subject (關鍵詞) 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 (題名) Mitigating the systematic errors of E-GPS leveling using neural network method
dc.type (資料類型) conferenceen