GPS position time-series analysis based on asymptotic normality of M-estimation |
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Authors: | Email author" target="_blank">A?KhodabandehEmail author A?R?Amiri-Simkooei M?A?Sharifi |
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Institution: | (1) Proudman Oceanographic Laboratory, 6 Brownlow Street, Liverpool, L3 5DA, UK |
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Abstract: | The efficacy of robust M-estimators is a well-known issue when dealing with observational blunders. When the number of observations
is considerably large—long time series for instance—one can take advantage of the asymptotic normality of the M-estimation
and compute reasonable estimates for the unknown parameters of interest. A few leading M-estimators have been employed to
identify the most likely functional model for GPS coordinate time series. This includes the simultaneous detection of periodic
patterns and offsets in the GPS time series. Estimates of white noise, flicker noise, and random walk noise components are
also achieved using the robust M-estimators of (co)variance components, developed in the framework of the least-squares variance
component estimation (LS-VCE) theory. The method allows one to compute confidence interval for the (co)variance components
in asymptotic sense. Simulated time series using white noise plus flicker noise show that the estimates of random walk noise
fluctuate more than those of flicker noise for different M-estimators. This is because random walk noise is not an appropriate
noise structure for the series. The same phenomenon is observed using the results of real GPS time series, which implies that
the combination of white plus flicker noise is well described for GPS time series. Some of the estimated noise components
of LS-VCE differ significantly from those of other M- estimators. This reveals that there are a large number of outliers in
the series. This conclusion is also affirmed by performing the statistical tests, which detect (large) parts of the outliers
but can also leave parts to be undetected. |
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