GPS动态变形监测中的多路径误差处理方法研究

发布时间：2018-03-06 21:01

本文选题：GPS　切入点：动态变形监测　出处：《中南大学》2013年硕士论文　论文类型：学位论文

【摘要】：对大型结构建筑物进行变形监测是把握其在施工运营阶段稳定性的必要措施,GPS由于定位精度高,测站之间无需通视,全天候观测,自动化程度高等优势,己成为当今最先进、使用最广泛的变形监测手段之一。在变形监测中,一般基线长度较短,电离层延迟等公共误差可以通过差分技术消除,但多路径效应在基线两端不具有相关性,无法通过差分技术消除,因此多路径效应是制约GPS变形监测精度的关键因素之一。针对此问题,本文主要从以下几个方面进行了研究： (1)研究了GPS多路径效应的产生机理,对GPS载波相位多路径效应的影响幅值、影响频率等特性进行了系统的分析。 (2)对比了多路径效应周日重复性周期的几种计算方法,实验结果表明,广播星历法、互相关系数最大法及均方根误差最小法三种方法计算得到的多路径效应周期具有一致性,采用实际计算得到的多路径重复周期进行恒星日滤波结果要优于标准恒星时周期。 (3)针对GPS高频动态变形监测中多路径效应误差存在很强的时间相关性的特点,给出一种基于一阶高斯马尔科夫过程的观测噪声函数模型估计方法,并推出基于此模型的扩充状态向量法和相邻时间组差法两种改进卡尔曼滤波方法,分别采用这两种方法及标准卡尔曼滤波处理一组GPS模拟动态数据,对结果进行了对比分析,实验结果表明这两种改进方法的去噪效果均优于标准卡尔曼滤波法,均能有效地削弱多路径效应的影响,提高定位精度。 (4)提出了基于小波与PCA相结合的GPS噪声改正方法。该方法先通过小波对坐标序列进行多尺度分解,对高频部分进行阂值化处理,削弱高频随机噪声,再采用PCA方法对相关性较强的多路径效应进行提取和消除。实测数据分析表明,该组合方法能有效地削弱多路径效应及高频随机噪声,较单一滤波方法具有一定的优越性。图28幅,表12个,参考文献61篇。
[Abstract]:Deformation monitoring of large structural buildings is a necessary measure to grasp its stability in construction and operation. GPS has become the most advanced because of its high positioning accuracy, no need for common viewing between stations, all-weather observation, high degree of automation, and so on. One of the most widely used deformation monitoring methods. In deformation monitoring, common errors such as short baseline length, ionospheric delay and other common errors can be eliminated by differential technique, but the multipath effect is not relevant at both ends of the baseline. The multipath effect is one of the key factors that restrict the accuracy of GPS deformation monitoring. In order to solve this problem, this paper mainly studies the following aspects:. 1) the mechanism of GPS multipath effect is studied, and the influence of GPS carrier phase multipath effect on amplitude and frequency is analyzed systematically. The experimental results show that the multipath effect periods calculated by the broadcast ephemeris method, the maximum correlation number method and the root mean square error method are consistent. The calculated multipath repeated period is better than the standard star time period for star day filtering. In view of the strong time correlation of the multipath effect error in the dynamic deformation monitoring of GPS, a method for estimating the observation noise function model based on the first-order Gao Si Markov process is presented. Two improved Kalman filtering methods, the extended state vector method based on this model and the adjacent time component difference method, are presented. The two methods and the standard Kalman filter are used to process a set of GPS simulation dynamic data respectively, and the results are compared and analyzed. The experimental results show that the two improved methods are better than the standard Kalman filter, which can effectively reduce the effect of the multipath effect and improve the positioning accuracy. (4) A method of GPS noise correction based on wavelet and PCA is proposed. Firstly, the coordinate sequence is decomposed by wavelet, and the high frequency part is treated with threshold value, which weakens the random noise of high frequency. Then the PCA method is used to extract and eliminate the multipath effect which has strong correlation. The analysis of the measured data shows that the combined method can effectively reduce the multipath effect and the high frequency random noise. Compared with the single filtering method, there are 28 figs, 12 tables and 61 references.
【学位授予单位】：中南大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：P228.4;TU196.1

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