Volume 44 Issue 10
Oct.  2019
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CUI Jiawu, ZHANG Xingfu, ZHOU Boyang, DU Xiangfeng, WEI Dehong. Improved step-by-step elimination method for optimal selection of GNSS/leveling points[J]. Geomatics and Information Science of Wuhan University, 2019, 44(10): 1505-1510. DOI: 10.13203/j.whugis20180074
Citation: CUI Jiawu, ZHANG Xingfu, ZHOU Boyang, DU Xiangfeng, WEI Dehong. Improved step-by-step elimination method for optimal selection of GNSS/leveling points[J]. Geomatics and Information Science of Wuhan University, 2019, 44(10): 1505-1510. DOI: 10.13203/j.whugis20180074
shu

Improved step-by-step elimination method for optimal selection of GNSS/leveling points

  • 1.

    Department of Surveying and Mapping, Guangdong University of Technology, Guangzhou 510006, China

  • 2.

    Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, China

Funds: 

The National Natural Science Foundation of China 41674006

The National Natural Science Foundation of China 41504013

More Information
  • Author Bio:

    CUI Jiawu, postgraduate, specializes in mearsurement data processing. E-mail: 864885814@qq.com

  • Corresponding author:

    ZHANG Xingfu, PhD, associate professor. E-mail: xfzhang77@163.com

  • Received Date: August 28, 2018
  • Published Date: October 04, 2019
    Graphical Abstract
    • Abstract
  • The reasonable selection of GNSS/leveling points is very important to GNSS height fitting, step-by-step elimination method is a good method for the optimization selection of joint-observation points. The traditional elimination method selects the GNSS/leveling points based on the minimum fitting error of height anomaly, which will easily lead to uneven joint-observation points. According to this view, this paper proposes to optimize the joint-observation point based on the area size of Thiessen polygons generated by the GNSS/leveling points, and on this basis to improve the traditional method, namely taking into account both the size of the height anomaly fitting error and the area size of polygons generated by Thiessen method (referred to as the synthesis method). The experimental results show that the synthesis method can improve the distribution of joint-observation points, and get the fitting results of height anomaly with high stability and accuracy.
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    • References (21)
  • [1]
    王乐洋, 吴飞, 吴良才. GPS高程转换的总体最小二乘拟合推估模型[J].武汉大学学报·信息科学版, 2016, 41(9):1259-1264 http://ch.whu.edu.cn/CN/abstract/abstract5537.shtml

    Wang Leyang, Wu Fei, Wu Liangcai. Total Least Squares Fitting Estimation Model for GPS Height Transformation[J]. Geomatics and Information Science of Wuhan University, 2016, 41(9):1259-1264 http://ch.whu.edu.cn/CN/abstract/abstract5537.shtml
    [2]
    陈为民, 张旭东, 符华年, 等. GPS高程测量代替等级水准测量的应用研究[J].武汉大学学报·信息科学版, 2013, 38(7):828-831 http://ch.whu.edu.cn/CN/abstract/abstract2695.shtml

    Chen Weimin, Zhang Xudong, Fu Huanian, et al. Application Research of GPS Elevation Survey Instead of Grade Leveling[J]. Geomatics and Information Science of Wuhan University, 2013, 38(7):828-831 http://ch.whu.edu.cn/CN/abstract/abstract2695.shtml
    [3]
    张正禄, 邓勇, 罗长林, 等.利用GPS精化区域似大地水准面[J].大地测量与地球动力学, 2006, 26(4):14-17 http://d.old.wanfangdata.com.cn/Periodical/dkxbydz200604004

    Zhang Zhenglu, Deng Yong, Luo Changlin, et al. Refining Local Quasigeoid with GPS[J]. Journal of Geodesy and Geodynamics, 2006, 26(4):14-17 http://d.old.wanfangdata.com.cn/Periodical/dkxbydz200604004
    [4]
    刘大杰, 姚连璧, 周全基. GPS水准的拟合基准面高程系统[J].测绘学报, 2000, 29(S1):11-15 http://d.old.wanfangdata.com.cn/Periodical/chxb2000z1002

    Liu Dajie, Yao Lianbi, Zhou Quanji. Fit Height Basis for GPS Leveling[J]. Acta Geodaetica et Cartographica Sinica, 2000, 29(S1):11-15 http://d.old.wanfangdata.com.cn/Periodical/chxb2000z1002
    [5]
    高伟, 徐绍铨. GPS高程分区拟合转换正常高的研究[J].武汉大学学报·信息科学版, 2004, 29(10):908-911 http://ch.whu.edu.cn/CN/abstract/abstract4529.shtml

    Gao Wei, Xu Shaoquan. Subregional Fitting and Transforming GPS Height into Normal Height[J]. Geomatics and Information Science of Wuhan University, 2004, 29(10):908-911 http://ch.whu.edu.cn/CN/abstract/abstract4529.shtml
    [6]
    Kavzoglu T, Saka M H. Modelling local GPS/Leve-lling Geoid Undulations Using Artificial Neural Networks[J]. Journal of Geodesy, 2005, 78(9):520-527 doi: 10.1007/s00190-004-0420-3
    [7]
    Shrestha R, Nazir A, Dewitt B, et al. Surface Interpolation Techniques to Convert GPS Ellipsoid Heights to Elevations[J]. Surveying and Land Information Systems, 1993, 53(2):133-144
    [8]
    吴兆福, 宫鹏, 高飞, 等.基于支持向量机的GPS似大地水准面拟合[J].测绘学报, 2004, 33(4):303-306 doi: 10.3321/j.issn:1001-1595.2004.04.005

    Wu Zhaofu, Gong Peng, Gao Fei, et al. GPS Quasi Geoid Fitting Based on Support Vector Machine Technology[J]. Acta Geodaetica et Cartographica Sinica, 2004, 33(4):303-306 doi: 10.3321/j.issn:1001-1595.2004.04.005
    [9]
    Zhong D. Robust Estimation and Optimal Selection of Polynomial Parameters for the Interpolation of GPS Geoid Heights[J]. Journal of Geodesy, 1997, 71(9):552-561 doi: 10.1007/s001900050123
    [10]
    沈云中, 高达凯, 张兴福. GPS水准点优化选择法[J].工程勘察, 2004, 191(6):49-51 http://d.old.wanfangdata.com.cn/Conference/5106361

    Shen Yunzhong, Gao Dakai, Zhang Xingfu. GPS Leveling Optimization Method[J]. Geotechnical Investigation & Surveying, 2004, 191(6):49-51 http://d.old.wanfangdata.com.cn/Conference/5106361
    [11]
    管真, 陈剑杰, 尉伯虎.控制点数量与分布对GPS水准二次曲面拟合精度的影响[J].测绘与空间地理信息, 2012, 35(2):126-128 doi: 10.3969/j.issn.1672-5867.2012.02.038

    Guan Zhen, Chen Jianjie, Wei Bohu. Influence of Number and Distribution of Control Points on the GPS Leveling Fitting Accuracy by Conicoid Fitting Method[J]. Geomatics & Spatial Informatlon Technology, 2012, 35(2):126-128 doi: 10.3969/j.issn.1672-5867.2012.02.038
    [12]
    姚吉利, 曲国庆, 刘科利, 等.拟合点分布与GPS水准面拟合精度关系研究[J].大地测量与地球动力学, 2008, 28(5):50-54 http://d.old.wanfangdata.com.cn/Periodical/dkxbydz200805010

    Yao Jili, Qu Guoqing, Liu Keli, et al. Research on Accuracy of Quasi-geoid with Fitting Methods Under Different Distribution of GPS Points[J]. Journal of Geodesy and Geodynamics, 2008, 28(5):50-54 http://d.old.wanfangdata.com.cn/Periodical/dkxbydz200805010
    [13]
    楼立志.中国及邻区模拟大地水准面的研究[D].武汉: 中国科学院研究生院测量与地球物理研究所, 2004 http://cdmd.cnki.com.cn/article/cdmd-80057-2004097816.htm

    Lou Lizhi. A Study on Simulated Geoid in China and Its Adjacent Regions[D]. Wuhan: Institute of Geodesy and Geophysics, Chinese Academy of Scien-ces, 2004 http://cdmd.cnki.com.cn/article/cdmd-80057-2004097816.htm
    [14]
    Lin L S. Application of a Back-Propagation Artificial Neural Network to Regional Grid-based Geoid Model Generation Using GPS and Leveling Data[J]. Journal of Surveying Engineering, 2007, 133(2):81-89 doi: 10.1061/(ASCE)0733-9453(2007)133:2(81)
    [15]
    杨明清, 靳蕃, 朱达成, 等.用神经网络方法转换GPS高程[J].测绘学报, 1999, 28(4):301-307 doi: 10.3321/j.issn:1001-1595.1999.04.005

    Yang Mingqing, Jin Fan, Zhu Dacheng, et al. Conversion of GPS Height by Artifical Neural Network Method[J]. Acta Geodaetica et Cartographica Sinica, 1999, 28(4):301-307 doi: 10.3321/j.issn:1001-1595.1999.04.005
    [16]
    胡伍生, 华锡生, 张志伟.平坦地区转换GPS高程的混合转换方法[J].测绘学报, 2002, 31(2):128-133 doi: 10.3321/j.issn:1001-1595.2002.02.008

    Hu Wusheng, Hua Xisheng, Zhang Zhiwei. Conversion of GPS Height in Plainness Area by the CF & NNM Method[J]. Acta Geodaetica et Cartographica Sinica, 2002, 31(2):128-133 doi: 10.3321/j.issn:1001-1595.2002.02.008
    [17]
    张小红, 程世来, 许晓东.基于Kriging统计的GPS高程拟合方法研究[J].大地测量与地球动力学, 2007, 27(2):47-51 http://d.old.wanfangdata.com.cn/Periodical/dkxbydz200702009

    Zhang Xiaohong, Cheng Shilai, Xu Xiaodong. Research of GPS Elevation Fitting Models Based on Kriging Method[J]. Journal of Geodesy and Geodynamics, 2007, 27(2):47-51 http://d.old.wanfangdata.com.cn/Periodical/dkxbydz200702009
    [18]
    王殊伟, 李斐, 柯宝贵, 等.基于BP神经网络算法的GPS水准高程转换[J].武汉大学学报·信息科学版, 2009, 34(10):1190-1193, 1185 http://ch.whu.edu.cn/CN/abstract/abstract1404.shtml

    Wang Shuwei, Li fei, Ke Baogui, et al. Conversion of GPS Height Based on BP ANNs[J]. Geomatics and Information Science of Wuhan University, 2009, 34(10):1190-1193, 1185 http://ch.whu.edu.cn/CN/abstract/abstract1404.shtml
    [19]
    赵建虎, 王真祥, 王胜平, 等.大区域似大地水准面模型的建立方法研究[J].武汉大学学报·信息科学版, 2008, 33(1):68-71 http://ch.whu.edu.cn/CN/abstract/abstract1494.shtml

    Zhao Jianhu, Wang Zhenxiang, Wang Shengping, et al. Determination of a Quasi-geoid in Large Region with Datum of Continuous GPS Networks[J]. Geomatics and Information Science of Wuhan University, 2008, 33(1):68-71 http://ch.whu.edu.cn/CN/abstract/abstract1494.shtml
    [20]
    Thiessen A H. Precipitation Averages for Large Areas[J]. Monthly Weather Review, 1911, 39(7):1082-1089 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_870a02234a48ad79c225c051e3f2e809
    [21]
    中华人民共和国住房和城乡建设部, 中华人民共和国国家质量监督检验检疫总局. GB 50026-2007工程测量规范[S].北京: 中国计划出版社, 2008

    Ministry of Housing and Urban-Rural Development of the People's Republic of China, General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China. GB 50026-2007 Code for Engineering Surveying[S]. Beijing: China Planning Press, 2008
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