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| Titel |
Extracting white noise statistics in GPS coordinate time series |
| VerfasserIn |
J.-P. Montillet, P. Tregoning, S. McClusky, K. Yu |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250062678
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| Zusammenfassung |
| The noise in GPS coordinate time series is known to follow a power-law noise model with
different components (white noise, flicker noise, random-walk). This work proposes an
algorithm to estimate the white noise statistics, through the decomposition of the GPS
coordinate time series into a sequence of sub-time series using the Empirical Mode
Decomposition algorithm. The proposed algorithm estimates the Hurst parameter for each
sub time series, then selects the sub time series related to the white noise based on the Hurst
parameter threshold. The algorithm is applied to simulated GPS time series and real
data.
Both simulated GPS coordinate time series and real data are employed to test this new
method, results are compared to the standard (CATS software) Maximum Likelihood (ML)
estimator approach.
For a comparison with the Maximum Likelihood approach (CATS software), the number
of epochs for the selected GPS time series is varied between 3 and 8 years. The results are
promising when compared to CATS, but suffer from a larger standard deviation. The results
demonstrate that this proposed algorithm has very low computational complexity and can be
more than one hundred times faster than the CATS ML method, at the cost of a moderate
increase of the uncertainty (~ 5%) of the white noise amplitude. Reliable white noise
statistics are useful for a range of applications including improving the filtering of GPS
time series, checking the validity of estimated coseismic offsets and estimating
unbiased uncertainties of site velocities. The low complexity and computational
efficiency of the algorithm can greatly speed up the processing of geodetic time
series. |
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