Kriging Kalman滤波在变形监测中的应用

Application of Kriging Kalman filter to the deformation monitoring

在变形监测中,经常会出现有些目标点无法进行观测,或者测站观测值丢失的问题,常用的数据处理方法没有考虑观测点之间的空间相关性,以致得到的处理结果不能满足高精度的要求.结合变形监测的特点对KrigingKalman滤波进行研究,模拟实验显示,文中方法不仅可以对未知点进行准确预报,而且对已知时间序列的滤波精度比纯时间域标准Kalman滤波精度提高21％～46％.最后将Kriging Kalman滤波应用于五强溪大坝的变形监测数据处理.

In deformation monitoring, some aimed positions usually occur to fail the observation or some of the observed values are lost. Commonly-used data process methods usually don＇t take the space correlation among the sites into account. So the processed results can＇t meet the demand of high precision. Kriging Kalman filter is used to consider the feature of deformation monitoring. An experiment of simulation is conducted to show that this method not only has an accurate prediction of unobserved locations, but also has an improvement of 21%-46% in precision compared with Standard Kalman filter at monitored locations. At last, the method is applied to the data processing of deformation monitoring in Wuqiangxi dam successfully.