Analysis of common mode error of GNSS coordinate time series in Xinjiang with independent component analysis
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摘要: 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.
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关键词:
- 全球卫星导航系统(GNSS)坐标时间序列 /
- 主成分分析(PCA) /
- 独立分量分析(ICA) /
- 共模误差(CME) /
- 空间滤波
Abstract: 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. -
表 1 PCA、ICA滤波前后残差序列的平均RMS变化
mm 方
向滤波前RMS值 PCA滤波后RMS值 ICA滤波后RMS值 均值 最大值 最小值 均值 最大值 最小值 均值 最大值 最小值 N 1.995 3.821 1.454 1.148 3.596 0.572 1.395 3.631 0.791 E 1.786 3.262 1.141 0.962 3.210 0.416 1.238 2.146 0.619 U 7.527 10.880 5.793 3.844 8.071 1.960 4.860 9.516 3.479 
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表 2 测站振幅标准差统计
方向 滤波前/mm 滤波后/mm 滤波前后改善
的百分比/%年振幅
σ半年振幅
σ年振幅
σ半年振幅
σ年振幅
σ半年振幅
σN 0.159 0.110 0.089 0.089 44.49 45.33 E 0.179 0.125 0.119 0.119 32.89 33.07 U 0.672 0.470 0.383 0.383 43.12 43.18
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[1] 姜卫平, 王锴华, 李昭, 等. GNSS坐标时间序列分析理论与方法及展望[J]. 武汉大学学报:(信息科学版), 2018, 43(12): 2112-2123. [2] 党亚民, 杨强, 薛树强, 等. GNSS监测的川滇地区地壳形变动态变化特征[J]. 大地测量与地球动力学, 2019, 39(2): 111-116,117. [3] 姜卫平. 卫星导航定位基准站网的发展现状机遇与挑战[J]. 测绘学报, 2017, 46(10): 1379-1388. DOI: 10.11947/j.AGCS.2017.20170424 [4] WDOWINSKI S, BOCK Y, ZHANG J, et al. Southern California permanent GPS geodetic array: spatial filtering of daily positions for estimating coseismic and postseismic displacements induced by the 1992 Landers earthquake[J]. Journal of geophysical research: solid earth, 1997, 102(B8): 18057-18070. DOI: 10.1029/97JB01378 [5] 周茂盛, 郭金运, 沈毅, 等. 基于多通道奇异谱分析的GNSS坐标时间序列共模误差的提取[J]. 地球物理学报, 2018, 61(11): 4383-4395. DOI: 10.6038/cjg2018L0710 [6] 贺小星, 姜卫平, 周晓慧, 等. GPS坐标时间序列广义共模误差分离方法[J]. 测绘科学, 2018, 43(10): 7-15. [7] 侯争. GNSS地壳异常形变信息探测理论与方法研究[J]. 测绘学报, 2021, 50(6): 851. DOI: 10.11947/j.AGCS.2021.20200315 [8] FENG T F, SHEN Y Z, WANG F W. Independent component extraction from the incomplete coordinate time series of regional GNSS networks[J]. Sensors, 2021, 21(5): 1569-1584. DOI: 10.3390/s21051569 [9] MING F, YANG Y X, ZENG A M, et al. Spatiotemporal fltering for regional GPS network in China using independent component analysis[J]. Journal of geodesy, 2017, 91(4): 419-440. DOI: 10.1007/s00190-016-0973-y [10] BANERJEE C, KUMAR D N. Analyzing large-scale hydrologic processes using GRACE and hydrometeorolo-gical datasets[J]. Water resour manage, 2018(32): 4409-4423. DOI: 10.1007/s11269-018-2070-x [11] EBMEIER S K. Application of independent component analysis to multitemporal InSAR data with volcanic case studies[J]. Journal of geophysical research: solid earth, 2016, 121(12): 8970-8986. DOI: 10.1002/2016JB013765 [12] LIU N, DAI W J, SANTTERRE R, et al. A MATLAB based Kriged Kalman Filter software for interpolateing missing data in GNSS coordinate time series[J]. GPS solution, 2018, 22(1): 1841-1867. DOI: 10.1007/s10291-017-0689-3 [13] 贺小星, 孙喜文. PANGA坐标时间序列噪声模型特性分析[J]. 全球定位系统, 2018, 43(6): 69-75. [14] BOS M S, FERNANDES R M S, WILLIAMS S D, et al. Fast error analysis of continuous GNSS observations with missing data[J]. Journal of geodynamics, 2013, 87(4): 351-360. DOI: 10.1007/s00190-012-0605-0 [15] 马飞虎, 岳祥楠, 贺小星, 等. CME对IGS基准站坐标序列噪声模型及速度估计影响分析[J]. 全球定位系统, 2019, 44(5): 47-54. [16] JI L Y, ZHANG Y, WANG Q L, et al. Detecting land uplift associated with enhanced oil recovery using InSAR in the Karamay oil field, Xinjiang, China[J]. International journal of remote sensing, 2016, 37(7): 1527-1540. DOI: 10.1080/01431161.2016.1154222 [17] 戴海亮, 孙付平, 朱新慧. 中国区域内IGS站时间序列的非线性变化分析[J]. 全球定位系统, 2018, 43(6): 76-81. [18] 张双成, 李振宇, 何月帆, 等. GNSS高程时间序列周期项的经验模态分解提取[J]. 测绘科学, 2018, 43(8): 80-84,96. [19] 姜卫平, 夏传义, 李昭, 等. 环境负载对区域GPS基准站时间序列的影响分析[J]. 测绘学报, 2014, 43(12): 1217-1223. [20] GRUSZCZYNSKI M, BOGUSZ J, KLOS A. Orthogonal transformation in extracting of common mode errors from continuous GPS networks[J]. Acta geodynamica et geomaterialia, 2016, 13(3): 291-298. DOI: 10.13168/AGG.2016.0011 [21] HE X X, MONTILLET J P, FERNANDES R, et al. Review of current GPS methodologies for producing accurate time series and their error sources[J]. Journal of geodynamics, 2017(106): 12-29. DOI: 10.1016/j.jog.2017.01.004 -
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