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Title: Effects of linear trends on estimation of noise in GNSS position time-series

Journal Article · · Geophysical Journal International
DOI:https://doi.org/10.1093/gji/ggw391· OSTI ID:1340237
 [1];  [1];  [1]
  1. Stanford Univ., CA (United States). Dept. of Geophysics
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A thorough understanding of time-dependent noise in Global Navigation Satellite System (GNSS) position time-series is necessary for computing uncertainties in any signals found in the data. However, estimation of time-correlated noise is a challenging task and is complicated by the difficulty in separating noise from signal, the features of greatest interest in the time-series. In this study, we investigate how linear trends affect the estimation of noise in daily GNSS position time-series. We use synthetic time-series to study the relationship between linear trends and estimates of time-correlated noise for the six most commonly cited noise models. We find that the effects of added linear trends, or conversely de-trending, vary depending on the noise model. The commonly adopted model of random walk (RW), flicker noise (FN) and white noise (WN) is the most severely affected by de-trending, with estimates of low-amplitude RW most severely biased. FN plus WN is least affected by adding or removing trends. Non-integer power-law noise estimates are also less affected by de-trending, but are very sensitive to the addition of trend when the spectral index is less than one. We derive an analytical relationship between linear trends and the estimated RW variance for the special case of pure RW noise. Finally, overall, we find that to ascertain the correct noise model for GNSS position time-series and to estimate the correct noise parameters, it is important to have independent constraints on the actual trends in the data.

Research Organization:
Stanford Univ., CA (United States)
Sponsoring Organization:
USDOE; National Aeronautics and Space Administration (NASA); Southern California Earthquake Center (SCEC) (United States)
Grant/Contract Number:
AC04-94AL85000; 14-EARTH14R-47; 13057
OSTI ID:
1340237
Report Number(s):
SAND2016-3023J; 637622
Journal Information:
Geophysical Journal International, Vol. 208, Issue 1; ISSN 0956-540X
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 12 works
Citation information provided by
Web of Science

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Cited By (2)

Investigation of the noise properties at low frequencies in long GNSS time series journal March 2019
Densification of the ITRF2014 position and velocity solution in the Nordic and Baltic countries journal July 2019

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Related Subjects

58 GEOSCIENCES
97 MATHEMATICS AND COMPUTING
Time-series analysis
Transient deformation
noise estimation
GNSS