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机构地区:[1]中南大学测绘与国土信息工程系,长沙市410083 [2]香港理工大学土地测量与地理资讯学系,香港九龙红磡
出 处:《武汉大学学报(信息科学版)》2008年第7期718-722,共5页Geomatics and Information Science of Wuhan University
基 金:国家自然科学基金资助项目(40704002);香港特别行政区研究基金RGC资助项目(PolyU5138/04E)
摘 要:卫星高度角、载噪比及信号强度是反映GPS观测值质量的重要指标,基于这些指标的随机模型可进一步削弱GPS残余大气延迟、衍射以及多路径效应等误差的影响,但这些随机模型对不同误差的处理效果具有一定差异。为合理利用这些随机模型,利用实际观测数据进行了比较计算,结果表明信号强度和载噪比随机模型的效果基本一致,当观测数据中载噪比输出时,可用信号强度代替载噪比建立随机模型;信号强度和载噪比随机模型对削弱衍射误差十分有效,而高度角随机模型能更有效地削弱残余对流层延迟误差。The accuracies of different satellite observations are different since the effects of the atmosphere delay, multipath and diffraction are different; so the stochastic model is very important in precise dynamic GPS positioning. The satellite elevation angle, C/No (carrier- to-noise-power density ratio), signal strength are the important indices of the observation quality. The stochastic models based on these indices can mitigate the errors such as diffraction; but each model is suitable to process some kinds of errors. In order to use these models correctly, some computing tests using real GPS data are carried out. The results show that the model based on signal strength as power as the model based on C/No. It means that the signal strength can substitute C/No as the indea in SIGMA stochastic model when there is no C/No output in observation data. The comparsion of these models indicates that models based on signal strength and C/No can mitigate diffraction errors more, while the model based on satellite elevation angle can mitigate more residual tropospheric delay errors.
分 类 号:P228.41[天文地球—大地测量学与测量工程]
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