GPS观量随机模型的研究-大地测量学与测量工程专业毕业论文.docx

GPS观量随机模型的研究-大地测量学与测量工程专业毕业论文.docx

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GPS观量随机模型的研究-大地测量学与测量工程专业毕业论文

桂林工学院硕士学位论文=苎!篡I.· 桂林工学院硕士学位论文 =苎!篡I.· 一I 一I—I I苎!!!皇!!!!=!! ABSTRACT Precise kinematic GPS positioning iS one of the hotspots in current geodetic applications and has a comprehensive application outlook.It involves many theoretical problems,such as stochastic models of measurements,quality control,unknown parameter’S estimators and techniques of’ambiguity resolution.The core problem of precise kinematic GPS positioning is how to fix ambiguity,and the precise ambiguity floats and their covariance matrix are necessary for ambiguity resolution.A exact stochastic model can receive a more exactly ambiguity float and the corresponding matrix,and the efficiency and success rate of ambiguity resolution are also improve.Therefore,researching this problem not only has a scientific significance,but also has a great commercj a】va]ue. The text analyses GPS observation model,the kinds of 1 inear combination of observation,the characteristic of kinds of errors fountain and the adopt i ng measures in the observat ion,and processes the thorough research on BEI州ESE GPS Software Ms-Windows Version4.2.It adopts non—di fferential way and double—di fferential way,the positioning precision of the single point is decimeter level.1i1I裕is within 0.01-一0.02:If the ambiguity right and without week skip,the observation datum of a epoch——by—。epoch can make the short basel ine(1ess than 20km)positioning precision reach centimeter level.Therefore,we use this software to dispose the GPS observation data in the part of experiment。and use the correlat ive program to translate the result fi les, then extract the useful datas from the fi le by program whi ch compi led in Matlab.All of these work are provide for studying of stochastic model. By analyzing the main characteristics of the stochastic models used currently in an experiment,it can be seen that though the success rate of ambiguity resolution is relatively lower than others,as the stochastic model of equal—variance does not need any prior para

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