三维坐标转换的高斯-赫尔默特模型及其抗差解法

刘超, 王彬, 赵兴旺, 余学祥

刘超, 王彬, 赵兴旺, 余学祥. 三维坐标转换的高斯-赫尔默特模型及其抗差解法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(9): 1320-1327. DOI: 10.13203/j.whugis20160348
引用本文: 刘超, 王彬, 赵兴旺, 余学祥. 三维坐标转换的高斯-赫尔默特模型及其抗差解法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(9): 1320-1327. DOI: 10.13203/j.whugis20160348
LIU Chao, WANG Bin, ZHAO Xingwang, YU Xuexiang. Three-Dimensional Coordinate Transformation Model and Its Robust Estimation Method Under Gauss-Helmert Model[J]. Geomatics and Information Science of Wuhan University, 2018, 43(9): 1320-1327. DOI: 10.13203/j.whugis20160348
Citation: LIU Chao, WANG Bin, ZHAO Xingwang, YU Xuexiang. Three-Dimensional Coordinate Transformation Model and Its Robust Estimation Method Under Gauss-Helmert Model[J]. Geomatics and Information Science of Wuhan University, 2018, 43(9): 1320-1327. DOI: 10.13203/j.whugis20160348

三维坐标转换的高斯-赫尔默特模型及其抗差解法

基金项目: 

国家自然科学基金 41404004

国家自然科学基金 41474026

安徽省博士后基金 2015B044

安徽理工大学科研启动基金 11152

详细信息
    作者简介:

    刘超, 博士, 副教授, 主要从事变形数据分析理论与方法研究。chaoliu0202@gmail.com

  • 中图分类号: P207

Three-Dimensional Coordinate Transformation Model and Its Robust Estimation Method Under Gauss-Helmert Model

Funds: 

The National Natural Science Foundation of China 41404004

The National Natural Science Foundation of China 41474026

the Postdoctoral Science Foundation of Anhui Province of China 2015B044

the Anhui University of Science and Technology Foundation 11152

More Information
    Author Bio:

    LIU Chao, PhD, associate professor, specializes in deformation monitoring data analysis. E-mail:chaoliu0202@gmail.com

  • 摘要: 对三维坐标转换的高斯-赫尔默特(Gauss-Helmert,GH)模型,采用牛顿-高斯(Newton-Gauss)迭代算法构建了该模型的拉格朗日目标函数,推导了其解算方法,并给出了具体的计算步骤。在此基础上,考虑到可能出现的粗差对观测空间与结构空间的综合影响,基于标准化残差构造权因子函数,推导了该模型的抗差解法。仿真实验结果表明,GH模型用于三维坐标转换时不受旋转角度大小和其他附加条件限制,解算结果与现有算法一致,且估计参数的维数大大降低,计算效率有一定程度的提高;所提出的抗差解法效果良好,与现有基于整体最小二乘的三维坐标转换的抗差解法相比,表现出了更好的稳健性。
    Abstract: For three-dimensional coordinate transformation, it's impossible using the Gauss-Markov model to obtain optimal parameter estimation from the functional model with error in its coefficient matrix. On the other hand, errors-in-variables model has difficulty expressing the functional model, and partial errors-in-variables model is complex as well as too much parameters to be estimated for the quasi-observation method. Therefore, Gauss-Helmert model is employed for three-dimensional coor-dinate transformation. The target function of the proposed model is established based on Newton-Gauss iterative algorithm, and the estimated method and its derivation procedure also are presented in this paper. Beyond the above process, we proposed a new robust estimation method for the proposed model, which is based on the normalized residual error and takes the influence of gross error on both observation and structure spaces into consideration. Meanwhile, derivational process of statistical tests and iterative algorithm are presented. The simulation experiment results show that the proposed estimation method has the same accuracy as the traditional method, which has robust with angular dimension and other additional conditions, but less estimation parameters. In addition, the new robust estimation method has effective robustness when comparing with the other existing robust total least square methods for the coordinate transformation.
  • 图  1   方案5和6估值与真值的差值序列(3个粗差)

    Figure  1.   Difference Values Between Truth Values and Estimated Results of Schemes 5 and 6 with Three Outliers

    表  1   存在1个粗差时各方案的均方根误差对比

    Table  1   Root Mean Square Errors of Estimated Parameters for Different Schemes with One Outlier

    方案X0 /mY0 /mZ0 /mμ/(10-5 rad)α1 /(10-5 rad)α2 /(10-5 rad)α3 /(10-5 rad)
    方案119.302 110.579 615.580 12.6001.5101.1601.750
    方案23.922 12.666 33.613 20.6320.4050.3510.366
    方案33.92 212.666 33.613 20.6320.4050.3510.366
    方案46.470 33.981 95.938 81.0100.6440.5570.570
    方案54.502 93.163 64.381 60.6960.4530.4100.452
    方案64.194 92.907 24.106 60.6660.4350.3820.407
    下载: 导出CSV

    表  2   存在3个粗差时各方案的均方根误差对比

    Table  2   Root Mean Square Errors of Estimated Parameters for Different Schemes with Three Outliers

    方案X0 /mY0 /mZ0 /mμ/(10-5 rad)α1 /(10-5 rad)α2 /(10-5 rad)α3 /(10-5 rad)
    方案110.307 912.340 718.474 01.4601.3901.7301.130
    方案23.752 92.956 42.844 90.5070.2890.3110.417
    方案33.752 92.956 42.844 90.5070.2890.3110.417
    方案48.797 86.880 56.709 91.2300.6640.7700.952
    方案57.068 05.744 25.810 60.9780.5920.6280.798
    方案64.695 83.896 13.931 20.6710.4040.4130.518
    下载: 导出CSV

    表  3   存在5个粗差时各方案的均方根误差对比

    Table  3   Root Mean Square Errors of Estimated Parameters for Different Schemes with Five Outliers

    方案X0 /mY0 /mZ0 /mμ/(10-5 rad)α1 /(10-5 rad)α2 /(10-5 rad)α3 /(10-5 rad)
    方案112.748 611.219 615.764 92.2501.7901.1501.400
    方案23.414 03.380 14.576 30.6100.4690.3800.424
    方案33.414 03.380 14.576 30.6100.4690.3800.424
    方案410.173 610.600 614.184 51.9601.4801.1201.320
    方案58.900 88.467 611.919 81.5801.2900.9561.110
    方案66.064 66.039 37.999 31.0900.8180.6390.757
    下载: 导出CSV

    表  4   方案5和方案6计算检查点的均方根误差(1个粗差)/m

    Table  4   Root Mean Square Errors of Check Points for Schemes 5 and 6 with One Outlier/m

    方向方案点位1点位2点位3点位4点位5点位6点位7
    X方案50.079 20.081 30.072 90.077 60.081 50.080 30.080 3
    方案60.078 40.080 30.072 40.077 10.080 80.079 00.078 7
    Y方案50.096 20.040 70.076 90.039 80.065 60.068 20.041 0
    方案60.095 90.039 10.077 00.038 60.065 30.068 20.040 1
    Z方案50.084 80.052 20.067 50.059 80.077 60.082 80.057 3
    方案60.084 70.052 20.066 80.058 30.076 40.081 90.056 1
    下载: 导出CSV

    表  5   方案5和方案6计算检查点的均方根误差(3个粗差)/m

    Table  5   Root Mean Square Errors of Check Points for Schemes 5 and 6 with Three Outliers/m

    方向方案点位1点位2点位3点位4点位5点位6点位7
    X方案50.078 50.081 10.073 90.082 20.082 70.080 30.082 9
    方案60.077 80.078 50.071 70.078 20.080 40.078 50.078 0
    Y方案50.098 70.048 10.082 70.044 60.065 10.073 00.044 9
    方案60.094 60.041 90.078 40.038 70.063 90.070 70.039 2
    Z方案50.089 30.056 90.068 30.061 80.076 80.083 40.059 3
    方案60.087 10.052 90.066 20.057 60.076 20.082 10.057 2
    下载: 导出CSV

    表  6   方案5和方案6计算检查点的均方根误差(5个粗差)/m

    Table  6   Root Mean Square Errors of Check Points for Schemes 5 and 6 with Five Outliers/m

    方向方案点位1点位2点位3点位4点位5点位6点位7
    X方案50.084 40.09 220.080 60.089 30.087 30.090 70.097 6
    方案60.079 70.083 50.075 00.082 10.081 30.081 60.085 5
    Y方案50.104 00.057 70.087 70.052 20.072 80.079 40.057 6
    方案60.097 90.047 50.083 10.041 90.069 50.072 10.046 8
    Z方案50.099 50.066 10.078 10.073 40.085 40.087 90.068 1
    方案60.091 90.060 10.070 80.062 10.078 90.083 50.060 0
    下载: 导出CSV
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  • 收稿日期:  2016-12-27
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