Comprehensive Analysis of the Effects of Common Mode Error on the Position Time Series of a Regional GPS Network

2018 ◽  
Vol 176 (6) ◽  
pp. 2565-2579 ◽  
Author(s):  
Zhen Li ◽  
Jianping Yue ◽  
Wang Li ◽  
Dekai Lu ◽  
Jiyuan Hu
Keyword(s):  
Time Series ◽  
Comprehensive Analysis ◽  
Common Mode ◽  
Common Mode Error ◽  
Gps Network ◽  
Position Time Series

scholarly journals Extracting Common Mode Errors of Regional GNSS Position Time Series in the Presence of Missing Data by Variational Bayesian Principal Component Analysis

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2298 ◽  
Author(s):  
Wudong Li ◽  
Weiping Jiang ◽  
Zhao Li ◽  
Hua Chen ◽  
Qusen Chen ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Missing Data ◽  
Principal Component ◽  
Component Analysis ◽  
Common Mode ◽  
Variational Bayesian ◽  
North East ◽  
Position Time Series ◽  
Gnss Position Time Series

Removal of the common mode error (CME) is very important for the investigation of global navigation satellite systems’ (GNSS) error and the estimation of an accurate GNSS velocity field for geodynamic applications. The commonly used spatiotemporal filtering methods normally process the evenly spaced time series without missing data. In this article, we present the variational Bayesian principal component analysis (VBPCA) to estimate and extract CME from the incomplete GNSS position time series. The VBPCA method can naturally handle missing data in the Bayesian framework and utilizes the variational expectation-maximization iterative algorithm to search each principal subspace. Moreover, it could automatically select the optimal number of principal components for data reconstruction and avoid the overfitting problem. To evaluate the performance of the VBPCA algorithm for extracting CME, 44 continuous GNSS stations located in Southern California were selected. Compared to previous approaches, VBPCA could achieve better performance with lower CME relative errors when more missing data exists. Since the first principal component (PC) extracted by VBPCA is remarkably larger than the other components, and its corresponding spatial response presents nearly uniform distribution, we only use the first PC and its eigenvector to reconstruct the CME for each station. After filtering out CME, the interstation correlation coefficients are significantly reduced from 0.43, 0.46, and 0.38 to 0.11, 0.10, and 0.08, for the north, east, and up (NEU) components, respectively. The root mean square (RMS) values of the residual time series and the colored noise amplitudes for the NEU components are also greatly suppressed, with average reductions of 27.11%, 28.15%, and 23.28% for the former, and 49.90%, 54.56%, and 49.75% for the latter. Moreover, the velocity estimates are more reliable and precise after removing CME, with average uncertainty reductions of 51.95%, 57.31%, and 49.92% for the NEU components, respectively. All these results indicate that the VBPCA method is an alternative and efficient way to extract CME from regional GNSS position time series in the presence of missing data. Further work is still required to consider the effect of formal errors on the CME extraction during the VBPCA implementation.

scholarly journals Analysis of the Potential Contributors to Common Mode Error in Chuandian Region of China

2020 ◽  
Vol 12 (5) ◽  
pp. 751
Author(s):  
Weijie Tan ◽  
Junping Chen ◽  
Danan Dong ◽  
Weijing Qu ◽  
Xueqing Xu
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Principal Component ◽  
Mass Loading ◽  
Common Mode ◽  
Surface Mass ◽  
Common Mode Error ◽  
Comparison Results ◽  
Wide Range ◽  
Gps Time Series

Common mode error (CME) in Chuandian region of China is derived from 6-year continuous GPS time series and is identified by principal component analysis (PCA) method. It is revealed that the temporal behavior of the CME is not purely random, and contains unmodeled signals such as nonseasonal mass loadings. Its spatial distribution is quite uniform for all GPS sites in the region, and the first principal component, uniformly distributed in the region, has a spatial response of more than 70%. To further explore the potential contributors of CME, daily atmospheric mass loading and soil moisture mass loading effects are evaluated. Our results show that ~15% of CME can be explained by these daily surface mass loadings. The power spectral analysis is used to assess the CME. After removing atmospheric and soil moisture loadings from the CME, the power of the CME reduces in a wide range of frequencies. We also investigate the contribution of CME in GPS filtered residuals time series and it shows the Root Mean Squares (RMSs) of GPS time series are reduced by applying of the mass loading corrections in CME. These comparison results demonstrate that daily atmosphere pressure and the soil moisture mass loadings are a part of contributors to the CME in Chuandian region of China.

ICA Spatiotemporal Filtering Method and Its Application in GPS Deformation Monitoring

2012 ◽  
Vol 204-208 ◽  
pp. 2806-2812 ◽  
Author(s):  
Da Wei Huang ◽  
Wu Jiao Dai ◽  
Fei Xue Luo
Keyword(s):  
Simulated Data ◽  
Component Analysis ◽  
Deformation Monitoring ◽  
Common Mode ◽  
Spatiotemporal Filtering ◽  
Filtering Method ◽  
Independent Components ◽  
Common Mode Error ◽  
Gps Network ◽  
Filtering Effect

Principal component analysis (PCA) is a good method to be used in spatiotemporal filtering for regional GPS network. As an extension of PCA, independent component analysis(ICA) is also widely concerded in many fields of sciences and application researches. As a new spatiotemporal filtering method, the application of ICA in spatiotemporal filtering of the regional GPS network and GPS deformation monitoring is explored in this paper. The simulated data test shows the filtering effect of ICA is the same as PCA, both of the PCA and ICA can extract two independent components which implied in simulated common mode error. At the same time, the SCIGN data test shows the filtering effect of ICA is a litter worse than PCA, but ICA extracts not only one independent components as common mode error, it is not unique and independence that can not be provided by the PCA method. It also reflects the essence of common mode error of different station in independence. Therefore, ICA method can be applied to GPS deformation monitoring as a new spatiotemporal filtering method, the feasibility and advantage of ICA is demonstrated in the experiment of simulated data and SCIGN data.

scholarly journals Impacts of Local Effects and Surface Loads on the Common Mode Error Filtering in Continuous GPS Measurements in the Northwest of Yunnan Province, China

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5408
Author(s):  
Keliang Zhang ◽  
Yuebing Wang ◽  
Weijun Gan ◽  
Shiming Liang
Keyword(s):  
Time Series ◽  
Yunnan Province ◽  
Vertical Component ◽  
Mass Loading ◽  
Gps Measurements ◽  
Local Effects ◽  
Common Mode ◽  
Common Mode Error ◽  
Grace Data ◽  
The Common

While seasonal hydrological mass loading, derived from Gravity Recovery and Climate Experiment (GRACE) measurements, shows coherent spatial patterns and is an important source for the common mode error (CME) in continuous global positioning system (cGPS) measurements in Yunnan, it is a challenge to quantify local effects and detailed changes in daily GPS measurements by using GRACE data due to its low time and spatial resolutions. In this study, we computed and compared two groups of CMEs for nine cGPS sites in the northwest Yunnan province; rCMEs were computed with the residual cGPS time series having high inter-station correlations, while oCMEs were computed with all the GPS time series. The rCMEs-filtered time series had smaller variances and larger root mean square (RMS) reductions than those that were oCMEs-filtered, and when the stations local effects were not removed, spurious transient-like signals occurred. Compared with hydrological mass loading (HYDL), its combination with non-tidal atmosphere pressure and ocean mass reached a better agreement with the CME in the vertical component, with the Nash–Sutcliffe efficiency (NSE) increasing from 0.28 to 0.55 and the RMS reduction increasing from 15.19% to 33.4%, respectively. Our results suggest that it is necessary to evaluate the inter-station correlation and remove the possible noisy stations before conducting CME filtering, and that one should carefully choose surface loading models to correct the raw cGPS time series if CME filtering is not conducted.

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