一种快速建立GPS滑坡监测网卡尔曼滤波模型的方法研究  

Method for Fast Establishing Kalman Filtering Model in GPS Landslide Monitoring Network

作　　者：高雅萍[1] 冯晓亮[2] 

机构地区：[1]成都理工大学地球科学学院,成都610059 [2]中国地质科学院探矿工艺研究所,成都610081

出　　处：《工程勘察》2007年第2期54-56,共3页Geotechnical Investigation & Surveying

摘　　要：卡尔曼滤波是目前处理动态变形监测数据的常用方法,如何快捷迅速的建立合适的卡尔曼滤波模型,将直接影响到数据处理的速度和可信度,常用方法是时间序列分析法,但此方法需要大量的观测数据。本文探讨在观测数据样本不多地情况下利用位移变化速度数据来选取的GPS滑坡监测的卡尔曼滤波模型,并通过实测数据证明该方法切实可行。Kalman filtering is a common method of data processing in dynamic GPS deformation monitoring. But, how to rapidly establish an appropriate Kalman filtering function model will directly affect both the speed and the reliability of data processing. However, time series analysis is the usual way, it requires a lot of observation data. This paper adopts velocity variety data to select Kalman filtering function model of GPS landslide deformation monitoring in case that there is no sufficient observation data. This method has been testified by actual measuring data.

关 键 词：卡尔曼滤波 函数模型 时间序列分析法 

分 类 号：P228.4[天文地球—大地测量学与测量工程]