Kriging Kalman滤波在变形监测中的应用
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摘要
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Kriging Kalman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.
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.
引文
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[3]张福荣,王涛.自适应Kalman滤波在某大坝形变监测中的应用[J].地矿测绘,2011,27(1):10-12.
[4]李子阳,郭丽,顾冲时.大坝监测资料的时变Kalman预测模型[J].武汉大学学报:信息科学版,2010,35(8):991-995.
[5]郭丽,王启明,袁永生.Kalman滤波用于大坝位移模拟与预报[J].水电能源科学,2006,24(6):53-56.
[6]NOEL CRESSIE.Statistics for Spatial Data[M].A Wiley-Interscience Publication,1993.
[7]HUANG H-C,NOEL CRESSIS.Spatial-temporal prediction of snow water equivalent using the Kalman filter[J].Computational Statistics&Data Analysis,1996(22):159-175.
[2]修延霞,侯凯.卡尔曼滤波在大坝变形监测中的应用[J].城市勘测,2010(1):92-95.
[3]张福荣,王涛.自适应Kalman滤波在某大坝形变监测中的应用[J].地矿测绘,2011,27(1):10-12.
[4]李子阳,郭丽,顾冲时.大坝监测资料的时变Kalman预测模型[J].武汉大学学报:信息科学版,2010,35(8):991-995.
[5]郭丽,王启明,袁永生.Kalman滤波用于大坝位移模拟与预报[J].水电能源科学,2006,24(6):53-56.
[6]NOEL CRESSIE.Statistics for Spatial Data[M].A Wiley-Interscience Publication,1993.
[7]HUANG H-C,NOEL CRESSIS.Spatial-temporal prediction of snow water equivalent using the Kalman filter[J].Computational Statistics&Data Analysis,1996(22):159-175.