Accuracy enhancement of GPS time series using principal component analysis and block spatial filtering

He, Xingui; Hua, Xing; Yu, Kegen; Xuan, Wei; Lu, Tianlan; Zhang, W.; Chen, Xuqi

doi:10.1016/j.asr.2014.12.016

Cited by 66 publications

(55 citation statements)

References 40 publications

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“…The processing includes the following corrections: ATML, SMML, NTOL and SCML based on QLM dataset (Yuan et al, 2008;Jiang et al, 2013;He et al, 2015). The QLM dataset described as follows (for me detail see Dong et al, 2002;Yuan et al, 2008;Jiang et al, 2013;He et al, 2015).…”

Section: Environmental Loading Dataset and Processingmentioning

confidence: 99%

“…improved filtering algorithms, error correction models and optimized processing strategies), the impact of various factors (i.e. satellite and receiver clock errors, ionospheric delay, tropospheric delay), and multipath effects have been efficiently removed or greatly reduced (Bertiger et al, 2010;He et al, 2015). Apart from these random and systematic errors, site-position time series generated from continuous GPS stations reveal significant seasonal variations.…”

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confidence: 99%

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Noise analysis for environmental loading effect on GPS time series

He¹

2016

Acta Geodynamica Et Geomaterialia

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This paper focusses on the impact of environment mass loading on GPS time-series of position changes and noise characteristic. We make use of position time series of 206 GPS station ranging from 2001 to 2013 globally distributed. Firstly, we investigate the spatio-temporal pattern of mass loadings based on QLM dataset. The results show that loading effect is significant for short time series, and the instantaneous impact cannot be ignored especially in high-precision geophysical studies. Meanwhile, the results indicate that loading effect behaves with regional difference in spatial scale, and this explains why there exist significant differences on the contribution of environmental loading on GPS time series in the scientific community. Secondly, among 90 % of the stations the weighted root mean square (WRMS) is reduced after applying the loading correction based on QLM model, improving the accuracy of GPS time series. Finally, we quantify the stochastic of GPS time series. The results show that the noise model of GPS time series can be described by a various combination of those models, mainly by FN+WN model and PL+WN model. From our statistics, we can conclude that the best noise models are either white and flicker or white and power-law. Environment loading has a significant impact on the stochastic noise properties of GPS time series, on 33 %, 15 %, and 39 % of the station's noise properties (noise types) have been changed after loading correction for NEU components, respectively. Environment loading is one of the factor that 'raw' GPS time series exhibited Gauss Markov noise properties. Furthermore, the impact of environmental loading on GPS site velocity cannot be ignored; this is particularly significant for the vertical component. ARTICLE INFO

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Section: Environmental Loading Dataset and Processingmentioning

confidence: 99%

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confidence: 99%

Noise analysis for environmental loading effect on GPS time series

He¹

2016

Acta Geodynamica Et Geomaterialia

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“…How to define the optimal weighting function in spatiotemporal filtering is an open question [1]. Distances, correlations and formal errors are the factors that are usually considered in generating weights [2,20,21]. In our previous work, we realized the importance of formal errors, but this research was not comprehensive [26].…”

Section: Discussionmentioning

confidence: 99%

“…In previous studies, different methods have been employed, such as Stacking [17], PCA [1,21] and ICA [3]. The observations in these studies were regarded as equally weighted, while in fact, the formal errors were different.…”

Section: Discussionmentioning

confidence: 99%

The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series

Shen

2018

Remote Sensing

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Abstract:In the daily operation of regional GNSS (Global Navigation Satellite System) networks, the formal errors of all stations' coordinate components are calculated. However, spatiotemporal filtering based on traditional Principal Component Analysis (PCA) for regional GNSS position time series does not take these formal errors into account. This paper developed a PCA-based approach to extract Common Mode Error (CME) from the position time series of a regional GNSS station network, where formal errors were applied to construct a weight factor. Because coordinate components with larger errors have smaller weight factors in extracting CME, the performance of our proposed approach was anticipated to be better than the traditional PCA approach. The position time series of 25 stations in the Yunnan Province, China, were analyzed using our approach, as well as the traditional PCA approach. The average errors of the residual time series after removing the CMEs with our approach were 1.30 mm, 1.72 mm, and 4.62 mm for North, East and Up components, and the reductions with respect to those of the original time series were 18.23%, 15.42%, and 18.25%, respectively. If CMEs were removed from the traditional PCA approach, the corresponding average errors were 1.34 mm, 1.81 mm, and 4.84 mm, with reductions of 15.84%, 10.86%, and 14.32%, respectively. Compared to the traditional PCA approach, the average errors of our approach were reduced by about 2.39%, 4.56%, and 3.93% in the North, East and Up components, respectively. Analysis of CME indicated that it mainly contained white and flicker noise. In the synthetic position time series with 500 repeated simulations, the CME extracted by our approach was closer to the true simulated values than those extracted by the traditional PCA approach, regardless of whether local effects were considered or not. Specifically, the mean root mean square (RMS) reduction of our approach, relative to PCA, ranged from 1.35% to 3.93%. Our simulations illustrated that the RMS reductions depended not only on the magnitude, but also the variation of the formal error series, which further highlights the necessity of considering formal errors in spatiotemporal filtering.

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“…The reflection of deep deformations can be observed in the movement of the Earth's surface in the area of the future earthquake. Abnormal movements of the Earth's surface after earthquakes represent the total magnitude, reflection of the motion of the energy accumulation and discharge periods (Rudenko et al, 2013;Altamimi, 2015;He et al, 2015;Métivier et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

The research of interrelation between seismic activity and modern vertical movements of the European permanent GNNS-stations

Tretyak¹

2018

Acta Geodynamica Et Geomaterialia

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Based on experimental studies using the "integral velocity index of the vertical displacement of the territory" and criterion of generalized seismicity, a close functional relationship was found between two parameters of different physical nature: seismic activity and altitude displacements of the Earth's surface in Europe. The mathematical correlation between the generalized parameters of the velocity altitude displacements on the territory and seismic activity is established according to seismic and GNSS stations for the period from 2000-2011 for the areas in Europe with close correlation between seismicity and velocity of vertical movements of the Earth's surface. ARTICLE INFO

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Accuracy enhancement of GPS time series using principal component analysis and block spatial filtering

Cited by 66 publications

References 40 publications

Noise analysis for environmental loading effect on GPS time series

Noise analysis for environmental loading effect on GPS time series

The Consideration of Formal Errors in Spatiotemporal Filtering Using Principal Component Analysis for Regional GNSS Position Time Series

The research of interrelation between seismic activity and modern vertical movements of the European permanent GNNS-stations

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