摘要
基于全球分布均匀且时间跨度大于10a的138个IGS基准站坐标时间序列,分析了大空间尺度GPS网基准站坐标时间序列之间的相关性,发现部分测站之间的距离超过5 000km时仍存在较显著的相关性。针对目前共模误差提取方法存在的不足,引入相关系数作为权重因子,改进了区域叠加滤波算法。并利用IGS基准站坐标时间序列验证了此方法。结果表明,改进后的相关系数加权叠加滤波算法能够有效地提取大空间尺度GPS网坐标时间序列中的共模误差。
Based on 138 evenly distributed IGS coordinate time series spanning more than 10 years, site-to-site coefficents in large scale GPS network were analyzed. Their site-to-site coefficients indicate significant corelationship even when the between-site distance more than 5 000 km. Therefore, this paper proposes a new method introducing coefficients as weight factor to calculate common mode error, and it improved the correlation-based spatial filtering technique. Time series from global and regional networks were used to verify this new method. The results show that this improved coefficientbased spatial filtering technique overcomes the spatial scale limit and effectively extracts common mode error of GPS coordinates time series.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2014年第10期1168-1173,共6页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金资助项目(41304007)
中央高校基本科研业务费专项基金资助项目(121103)
武汉大学地球空间环境与大地测量教育部重点实验室测绘基础研究基金资助项目(11-02-02)~~
关键词
GPS坐标时间序列
共模误差
相关系数
GPS coordinate time series
common mode error
correlation coefficient
