摘要
GNSS时间序列中存在着一种与空间距离相关的共模误差,有效地剔除共模误差有利于获得更准确的速度场信息。区域堆栈滤波算法的共模误差剔除效果会受到区域网测站数量与空间尺度的影响,相关系数堆栈滤波算法也存在空间尺度阈值选取的问题。为了削弱空间尺度对区域叠加滤波的影响,引入距离反比因子与相关系数相结合,并尝试采用Spearman秩相关系数代替原有的皮尔逊系数,设计了不同组合方案的区域堆栈滤波算法。选取太平洋西北大地测量阵列(PANGA)的60个测站进行研究,结果表明距离反比因子和相关系数相结合的区域堆栈滤波方法能更好地剔除共模误差,基于Spearman秩相关系数的滤波方案与原有皮尔逊系数滤波算法效果相当。采用距离因子与相关系数相结合的滤波算法进行共模误差剔除,使得时间序列残差在水平方向降低约40%,高程方向降低约33%,速度场精度水平方向提高约38%,高程方向提高约30%。
There is a common mode error related to the spatial distance in the GNSS time series.Effectively eliminating the common mode error is beneficial to obtain more accurate velocity field information.The common-mode error elimination effect of the regional stack filter algorithm is affected by the number of regional network stations and the spatial scale,and the selection of the spatial scale threshold also affects the correlation coefficient stack filter algorithm.In order to weaken the influence of spatial scale on the regional superposition filter,the inverse distance factor is combined with the correlation coefficient,and the Spearman rank correlation coefficient is used to replace the original Pearson coefficient,and regional stack filtering algorithms with different combinations are designed.The 60 stations in the Pacific Northwest Geodetic Array(PANGA)are selected for the research.The results show that the regional stack filter method combining the inverse distance factor and the correlation coefficient can better eliminate common mode errors.The filter algorithm based on Spearman correlation coefficient has the same effect as that of Pearson correlation coefficient filtering algorithm.Common mode error elimination is performed by a filtering algorithm combining distance factor and correlation coefficient,which reduces the time series residual error by 40%in the horizontal direction,33%in the elevation direction,and improved the accuracy of the velocity field in the horizontal direction by 38%and in the elevation direction by 30%.
作者
王方超
吕志平
邝英才
李林阳
许炜
WANG Fangchao;LYU Zhiping;KUANG Yingcai;LI Linyang;XU Wei(Information Engineering University,Zhengzhou 450001,China)
出处
《测绘科学技术学报》
北大核心
2020年第6期562-567,共6页
Journal of Geomatics Science and Technology
基金
国家自然科学基金项目(41804006,41674019)
大地测量与地球动力学国家重点实验室开放基金(SKLGED2020-03-03-E)。
关键词
坐标时间序列
共模误差
相关系数
堆栈滤波
距离因子
coordinate time series
common mode error
correlation coefficient
stack filter
distance factor