| 基于比例整体最小二乘的GPS高程拟合 |
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| 引用本文： | 楚 彬,范东明.基于比例整体最小二乘的GPS高程拟合[J].测绘工程,2014(4):37-39. |
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| 作者姓名： | 楚 彬 范东明 |
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| 作者单位： | 西南交通大学地球科学与环境工程学院,四川成都610031 |
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| 基金项目： | 中央高校基本科研业务费专项资金（SWJTU10ZT02） |
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| 摘 要： | 针对GPS高程拟合过程中GPS基线观测量和水准高程观测量含有误差且残差中误差不相同的情况,在整体最小二乘(TLS)基础上引入比例因子λ来确定残差中误差的大小,即比例整体最小二乘(STLS)。实例计算表明,STLS比TLS和LS能够得到更好的估计参数,高程异常值拟合精度也相应提高。
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| 关 键 词： | GPS高程拟合 高程异常 EIV模型 比例整体最小二乘(STLS) 奇异值分解(SVD) |
GPS elevation fitting based on scaled total least squares |
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| CHU Bin,FAN Dong-ming.GPS elevation fitting based on scaled total least squares[J].Engineering of Surveying and Mapping,2014(4):37-39. |
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| Authors: | CHU Bin FAN Dong-ming |
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| Institution: | (School of Earth Sciences and Environmental Engineering,Southwest Jiaotong University, Chengdu 610031,China) |
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| Abstract: | In GPS elevation fitting, the baseline observations and leveling observations both contain errors and their residual errors are not the same. In order to deal with this problem, scaled total least squares （STLS） based on total least squares （TLS） is introduced. The result shows that the STLS is more effective in estimating the model parameters than TLS and LS, and the elevation anomaly fitting accuracy has been increased. |
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| Keywords: | GPS elevation fitting height anomaly Error-In-Variables model scaled total least squares singular value decomposition |
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