一种抗差的形变数据插补方法

发布时间：2018-03-07 16:31

  本文选题：缺失数据　切入点：插补　出处：《测绘科学》2017年09期 　论文类型：期刊论文

【摘要】：针对传统基于空间插值和时间序列上的插值补全形变缺失数据的方法在空间点位分布稀疏、观测值连续缺失以及含有粗差的情况下插补效果不佳的问题,提出了一种基于抗差Kriged Kalman Filter的形变缺失数据插补方法。该方法是一种时空插值的算法,在空间点位分布稀疏时考虑时间上的相关性,在时间上出现连续缺失时考虑其他点位对插补点的影响,以提高插补缺失数据的精度。又将抗差估计融合到Kriged Kalman Filter中以抵抗形变数据中粗差对插补精度的影响。利用模拟数据及天津GPS地面沉降数据进行了实验分析。结果表明:由于该法考虑了监测点的时空相关性以及具有抗差性能,使得插补结果在空间点位稀疏、连续缺失或具有粗差的情况下都具有较高的插补精度。
[Abstract]:The traditional interpolation method based on spatial interpolation and time series is used to solve the problem of sparse distribution of spatial points, continuous absence of observation values and poor interpolation effect in the case of gross error. A deformation-missing data interpolation method based on robust Kriged Kalman Filter is proposed, which is a spatio-temporal interpolation algorithm, which considers the temporal correlation when the spatial point distribution is sparse. Considering the effect of other points on the interpolation point when there is a continuous loss in time, In order to improve the accuracy of interpolation missing data, the robust estimation is fused into Kriged Kalman Filter to resist the influence of gross error in deformation data on interpolation accuracy. The simulation data and Tianjin GPS ground subsidence data are used for experimental analysis. The results show that the method takes into account the spatio-temporal correlation of monitoring points and its robust performance. The interpolation results have higher interpolation accuracy when the space points are sparse, continuously missing or with gross errors.
【作者单位】： 中南大学测绘与遥感科学系;湖南省精密工程测量与形变灾害监测重点实验室;
【基金】：国家“973”项目(2013CB733303) 中南大学教师研究基金项目(2014JSJJ003)
【分类号】：P207
，

本文编号：1580062