Noise behavior in CGPS position time series: the eastern North America case study

Abstract

We analyzed the noise characteristics of 112 continuously operating GPS stations in eastern North America using the Spectral Analysis and the Maximum Likelihood Estimation (MLE) methods. Results of both methods show that the combination of white plus flicker noise is the best model for describing the stochastic part of the position time series. We explored this further using the MLE in the time domain by testing noise models of (a) powerlaw, (b) white, (c) white plus flicker, (d) white plus randomwalk, and (e) white plus flicker plus random-walk. The results show that amplitudes of all noise models are smallest in the north direction and largest in the vertical direction. While amplitudes of white noise model in (c-e) are almost equal across the study area, they are prevailed by the flicker and Random-walk noise for all directions. Assuming flicker noise model increases uncertainties of the estimated velocities by a factor of 5-38 compared to the white noise model.

The Noise Properties and Velocities from a Time-Series of Estonian Permanent GNSS Stations

“…Understanding the noise properties of GNSS time-series is crucial for realistic uncertainty estimation of unknown parameters. Several studies have shown that the assumption of the time-uncorrelated white noise leads to underestimation of the velocity uncertainties [20,21,23,25]. Accordingly, it is best to use a combination of the time-correlated colored noise plus white noise to describe noise in GNSS time-series.…”

“…Generally, the noise in GNSS time-series has been described as the combination of colored noise and white noise [21]. It was found that the combination of flicker noise plus white noise models approximates well the noise properties of all three coordinate components [22][23][24][25]. However, other models like power law noise plus white noise or flicker noise plus random walk noise also provide precise solutions for the noise properties of the data [20,21,26].…”

“…However, other models like power law noise plus white noise or flicker noise plus random walk noise also provide precise solutions for the noise properties of the data [20,21,26]. Langbein [26] and Goudarzi et al [25] have shown that white noise is associated with the GNSS hardware noise and measurement errors, flicker noise with the GNSS system (i.e., unmodeled tropospheric delay and satellite ephemeris), and random walk noise is generally associated with the random motions of the station's monument [25,26].…”

Preliminary assessment of the effect of noise on velocity uncertainty on the Nigerian permanent GNSS network

“…part mostly considered as observational noise can be described by noise models (Goudarzi et al, 2015). Most of the stochastic parts of GNSS time series are time correlated.…”

“…The stochastic as done by (Klos et al, 2014;He et al, 2016;Xu andYue, 2017) Klos et al (2014) who analysed more than 40 stations belonging to the ASG-EUPOS and EPN networks with 5 years of observations from the area of Sudeten, concluded that the WN+PL noise best describes the error sources for most of the analysed stations. Elsewhere, Goudarzi et al (2015) analysed the behaviour of noise in 112 continuously operating GPS (CGPS) position time series in the eastern part of North America and found out that WN+FN is the best model that describes the stochastic part of the position time series. Xu and Yue (2017) in their assessment of the noise characteristics of daily position time series from 12 International GNSS Service sites located in China concluded that the noise model of most sites can be characterized by a combination of WN+FN.…”