Noise in multivariate GPS position time-series

By Alireza Amiri-Simkooei. 2009

108 citations recorded by the source corpus.

A methodology is developed to analyze a multivariate linear model, which occurs in many geodetic and geophysical applications. Proper analysis of multivariate GPS coordinate time-series is considered to be an application. General, special, and more practical stochastic models are adopted to assess the noise characteristics of multivariate time-series. The least-squares variance component estimation (LS-VCE) is applied to estimate full covariance matrices among different series. For the special model, it is shown that the multivariate time-series can be estimated separately, and that the (cross) correlation between series propagates directly into the correlation between the corresponding parameters in the functional model. The time-series of five permanent GPS stations are used to show how the correlation between series propagates into the site velocities. The results subsequently conclude that the general model is close to the more practical model, for which an iterative algorithm is presented. The results also indicate that the correlation between series of different coordinate components per station is not significant. However, the spatial correlation between different stations for individual components is significant (a correlation of 0.9 over short baselines) both for white and for colored noise components.

Subfields: Aerospace Engineering, Geophysics, Oceanography

View Paper: https://www.semanticscholar.org/paper/19d743b5d477651c688c8163607b51bc405635ad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Yuanxi Yang
2. Ksenia Dmitrieva
3. Paul Segall
4. C. Demets
5. Alireza Amiri-Simkooei
6. Zbigniew Wisniewski
7. Guobin Chang
8. Weiguang Gao

In Geophysics

1. Ksenia Dmitrieva
2. Paul Segall
3. C. Demets
4. Alireza Amiri-Simkooei
5. Yunzhong Shen
6. Bofeng Li

In Oceanography

1. Ksenia Dmitrieva
2. Paul Segall
3. C. Demets
4. Alireza Amiri-Simkooei
5. Yuanxi Yang
6. Yunzhong Shen

Citations in the corpus

• Spatiotemporal filtering for regional GPS network in China using independent component analysis (2017, background): “However, previous findings have shown that the CME contains colored noise (Williams et al. 2004; Amiri-Simkooei 2009; Wang et al. 2012; Dmitrieva et al. 2015).”
• Network-based estimation of time-dependent noise in GPS position time series (2015, background): “4.2 Common-mode errors There is considerable evidence that flicker and white noise are spatially correlated (Amiri-Simkooei 2009).”
• Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis (2013, background): “…(Shen and Li 2011), the maximum likelihood estimation (Zhang et al. 1997; Langbein and Johnson 1997; Mao et al. 1999; Williams 2003; Williams et al. 2004; Langbein 2004; Williams and Willis 2006) or the least squares variance component estimation (Amiri-Simkooei et al. 2007; Amiri-Simkooei 2009).”
• On the nature of GPS draconitic year periodic pattern in multivariate position time series (2013, methodology): “[29] The matrices Σ and Q are estimated using the multivariate noise assessment technique in an iterative procedure [see Amiri-Simkooei, 2009, algorithm in Figure 1].”