基于小波分析的自适应卡尔曼滤波在地铁变形监测中的应用

发布时间：2018-06-07 05:00

本文选题：地铁 + 测量机器人　；参考：《长安大学》2015年硕士论文

【摘要】：地铁,作为地下交通是一种时代的产物。它是在城市人口急剧增加、交通工具总量日益增长的今天,为了解决交通拥堵的现象、为了给人们带来快捷、便利、有序的乘车环境而开辟的又一道“捷径”。但是,地铁在给人们带来这诸多便利的同时也有不少问题需要我们去探究。在穿越既有线路、高大建筑物、深基坑等工程时,都会使原有的受力平衡被打破而发生一定的变形。如果这些因素不能得到及时的控制,都会导致严重的后果。因此,对地铁进行重点区域、多方面、多层次的监测以了解其整体状况就显得非常有必要。目前,主要还是进行地铁隧道的局部监测。对于每个监测区域都有比较多的测量方法、测量仪器以及数据整理方法。本文则主要是针对隧道口断面上的监测点,利用测量机器人智能化、自动化的方法对数据进行获取,再将数据进行小波预处理、方差补偿自适应卡尔曼滤波的方法,进而对其进行形变量的预测。主要的研究内容有:(1)阐述地铁隧道变形监测技术及其数据处理方法现状,根据具体项目要求提出测量机器人的数据获取方式及其数据处理方法;(2)介绍了测量机器人的基本结构和功能。通过对其系统组成、软件开发还有GeoBASIC、ASCII码、GeoCOM等接口的认识与学习,掌握了TS30的内部结构、程序开发流程及监测系统模块;(3)通过模拟实验进行仪器操作流程的学习,并对极坐标法进行包括精度分析、差分改正、基准点稳定性分析等的深入研究,并将测量结果利用传统的平差方法进行计算作为后面处理结果的对比数据;(4)了解回归分析、时间序列、灰色模型等常用的监测数据处理方法,并通过对应用比较广泛的小波去噪、Kalman滤波原理的全面学习,找到适合地铁监测的“基于小波分析的方差补偿自适应卡尔曼滤波”模型。通过matlab编程实现两种方法的滤波;最后,通过对上述知识的整理,以深圳市后亭地铁监测项目为依托进行所构建模型的对比分析与验证。经过数据检核可以知道该模型不仅可以应用在地铁这种形变量较小的工程,同样适合滑坡、版块儿移动等形变量较大的工程。
[Abstract]:Subway, as underground traffic is a product of the times. In order to solve the problem of traffic congestion and bring people a quick, convenient and orderly riding environment, it is another "shortcut" to solve the problem of traffic jams and to open up another "shortcut" to the rapid increase of urban population and the increasing of the total number of vehicles. However, subway has brought us many conveniences, but also many problems need to be explored. When crossing the existing lines, tall buildings, deep foundation pit and other projects, the original force balance will be broken and some deformation will occur. If these factors are not controlled in a timely manner, they can lead to serious consequences. Therefore, it is very necessary to monitor the subway in various areas and levels to understand its overall situation. At present, the main part of the subway tunnel monitoring. For each monitoring area, there are more measurement methods, measuring instruments and data processing methods. This paper is mainly aimed at the monitoring points in the tunnel section, using the intelligent and automatic method of measuring robot to obtain the data, then the data are preprocessed by wavelet, and the method of adaptive Kalman filter with variance compensation is used. Then the shape variables are predicted. The main research contents are: (1) expounding the present situation of deformation monitoring technology and data processing method of subway tunnel. According to the requirements of the project, the data acquisition method and data processing method of the measuring robot are presented. (2) the basic structure and function of the measuring robot are introduced. Through the understanding and learning of the system composition, the software development and the interfaces such as GeoBASIC ASCII code and GeoCOM, the internal structure of TS30, the program development process and the monitoring system module are mastered. And the polar coordinate method including precision analysis, difference correction, reference point stability analysis and so on, and the measurement results are calculated by the traditional adjustment method as the contrast data of the later processing results to find out the regression analysis. Time series, grey model and other commonly used monitoring data processing methods, and through the more widely used wavelet denoising Kalman filtering principle of a comprehensive study, The adaptive Kalman filter model of variance compensation based on wavelet analysis is found for subway monitoring. Through matlab programming to realize the filtering of the two methods; finally, through the above knowledge collation, based on the Shenzhen Houting subway monitoring project to build the model of comparative analysis and verification. It can be found that the model can be used not only in the engineering with small deformation amount of subway, but also in the engineering with large deformation such as landslide, plate movement and so on.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U231;U456.3

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