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组合模型的GPS高程转换及精度分析
引用本文:郭辉.组合模型的GPS高程转换及精度分析[J].测绘科学,2018(2):34-38.
作者姓名:郭辉
作者单位:安徽理工大学测绘学院,安徽淮南232001;中国矿业大学(北京)地球科学与测绘工程学院,北京 100083
基金项目:中国煤炭工业协会2015年度科学技术研究指导性计划项目
摘    要:针对二次曲面模型在地形起伏较大区域用于GPS高程转换中存在较大的模型误差的问题,该文构建了二次曲面-RBF神经网络组合的GPS高程转换模型,组合模型中用二次曲面拟合高程异常中的中长波项,用RBF神经网络来泛化高程异常去除中长波后的残余项,并进行了二次曲面模型、RBF神经网络模型及二次曲面-RBF组合模型的实测数据GPS高程转换、比较分析与精度评定。实例结果表明:该组合模型比二次曲面模型的转换精度提高了22%,比RBF神经网络模型的转换精度提高了40%,该组合模型的转换方法可行、精度优于单一模型。

关 键 词:组合模型  GPS高程  RBF神经网络  二次曲面拟合  高程异常  combination  model  GPS  height  radial  basis  function  neural  network  quadratic  surface  fitting  abnormal  height

Transformation of GPS height and accuracy analysis based on combination model
GUO Hui.Transformation of GPS height and accuracy analysis based on combination model[J].Science of Surveying and Mapping,2018(2):34-38.
Authors:GUO Hui
Abstract:Aiming at big model error of quadric surface for GPS conversion in the region of terrain fluctuation,the combination model of quadric surface and RBF neural network for GPS height was established,in which quadric surface was used to fit the medium-long term,and RBF neural network to fit the residual after long wave being removed in abnormal height.On the basis of project data,the quadric surface,RBF neural network,and combination model were used in doing GPS height conversion,and transformation accuracy of GPS height was analyzed and evaluated.The project example showed that the combination model accuracy was improved by 22 % than that of quadric surface model,by 40 % than that of RBF neural network model,and transformation method based on combination model was feasible and had higher accuracy.
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