基于独立分量法的新疆GNSS时间序列共模误差分析CSCD

共模误差(CME)是区域连续全球卫星导航系统(GNSS)网中的主要误差来源之一.针对GNSS时间序列具有非高斯分布特征,基于二阶统计量的主成分分析(PCA)难以准确提取出CME分量问题,采用具有高阶统计量的独立分量分析(ICA)对CME进行提取.以2011—2018年新疆区域GNSS坐标时间序列为例,将PCA滤波效果进行对比验证,分析了CME对GNSS坐标时间序列的影响,并对CME序列进行周期分析.结果表明:前6个独立分量包含CME分量,这可能与卫星轨道、地表质量负荷和时钟误差有关,ICA滤波后东(N)、北(E)、天顶(U)三个方向的均方根(RMS)值分别降低31.83%、32.29%、35.49%,速度不确定度分别降低44.14%、38.49%、43.32%,各测站的周期项振幅较滤波前更一致,有效地剔除了CME,提高了坐标时间序列的精度.

Analysis of common mode error of GNSS coordinate time series in Xinjiang with independent component analysis

The common mode error (CME) is one of the major error sources in the regional Global Navigation Satellite System (GNSS) network. Aiming at the problem that GNSS time series is subject to no-Gaussian distribution, and the principal component analysis (PCA) with second-order is inaccurately employed to separate the CME. In this paper, the independent component analysis (ICA) introduces high-order statistics to extract the CME. The effectiveness of the method is validated by processing the data of GNSS stations from 2011 to 2018 in Xinjiang, China, and then compared and verified the filtering effect of PCA. We analysis the influence of the CME for GNSS coordinate time series and the yearly signal of the CME. The results show that the CME mainly consists of the 6th independent components and can be attributed to satellite orbit, surface mass loading, and clock errors. After the ICA filtering, the reduction of mean RMS is 31.83%, 32.29%, 35.49% for the north (N), east (E), and up (U) components, respectively. The reduction of velocity uncertainty can achieve 44.14%, 38.49% and 35.49% in three components. In addition, the yearly amplitude of each GNSS station is more consistent that before spatiotemporal filtering, indicating that the ICA can effectively extract the CME and further improve the accuracy of coordinate time series.