| 补偿最小二乘估计在确定高程异常中的应用 |
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| 引用本文： | 高宁,高彩云,徐长海.补偿最小二乘估计在确定高程异常中的应用[J].测绘科学,2011,36(1):35-37. |
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| 作者姓名： | 高宁 高彩云 徐长海 |
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| 作者单位： | 河南城建学院测绘与城市空间信息系;宿州学院地理与环境科学系; |
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| 基金项目： | 国家测绘局重点实验室基金项目(KLM200818); 河南省教育厅2009年自然科学研究基金项目(2009B170001); 河南省河南城建学院2008年院级重点基金资助项目 |
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| 摘 要： | 针对常规最小二乘拟合求解高程异常存在的模型误差,本文提出将模型误差看作非参数信号采用补偿最小二乘法来处理,讨论了正则化矩阵R和平滑参数α的选取对拟合结果的影响,在对各种求解光滑参数深入研究的基础上,提出了一种Xu(α)函数法,并对一个测区的GPS水准数据进行解算,结果表明,利用补偿最小二乘模型求解高程异常优于最小二乘法。
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| 关 键 词： | 补偿最小二乘估计 高程异常 正则化矩阵R 平滑参数 αXu(α)函数 |
Application of penalized least squares estimation in height anomaly |
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| GAO Ning,GAO Cai-yun,XU Chang-hai.Application of penalized least squares estimation in height anomaly[J].Science of Surveying and Mapping,2011,36(1):35-37. |
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| Authors: | GAO Ning GAO Cai-yun XU Chang-hai |
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| Institution: | ②(①Department of Survey & Urban Spatial Information Engineering,Henan University of Urban Construction,Henan Pingdingshan 467044,China;②Department of Geography and Environment Science,Suzhou College,Suzhou 234000,China) |
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| Abstract: | The model errors exist inevitably in conventional least square fitting model of height anomaly,this article proposed that model error could be dealt with as nonparametric information using penalized least squares and discussed the effect of Regularizer R and Smoothing Parameter α on the results of fitting.Through the research on the solution of the Smoothing Parameter,a method of function Xu(α)was presented,and experimented on a GPS leveling measurement data.The Results showed that penalized least squares is better than least-square method in determining height anomaly. |
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| Keywords: | penalized least squares estimation height anomaly regularizer R smoothing parameter α function Xu(α) |
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