Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series

Various methods have been used to model the time-varying curves within the global positioning system (GPS) position time series. However, very few consider the level of noise a priori before the seasonal curves are estimated. This study is the first to consider the Wiener filter (WF), already used i...

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Published in:Mathematical geosciences 2019-01, Vol.51 (1), p.53-73
Main Authors: Klos, Anna, Bos, Machiel S., Fernandes, Rui M. S., Bogusz, Janusz
Format: Article
Language:English
Subjects:
Autoregressive processes
Chemistry and Earth Sciences
Computer Science
Earth and Environmental Science
Earth Sciences
Fast Fourier transformations
Fourier transforms
Geodesy
Geotechnical Engineering & Applied Earth Sciences
Global positioning systems
GPS
Gravity
Hydrogeology
Modelling
Noise
Noise levels
Noise reduction
Physics
Positioning systems
Satellite navigation systems
Signal processing
Statistics for Engineering
Time series
Wiener filtering
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container_title Mathematical geosciences
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creator Klos, Anna
Bos, Machiel S.
Fernandes, Rui M. S.
Bogusz, Janusz
description Various methods have been used to model the time-varying curves within the global positioning system (GPS) position time series. However, very few consider the level of noise a priori before the seasonal curves are estimated. This study is the first to consider the Wiener filter (WF), already used in geodesy to denoise gravity records, to model the seasonal signals in the GPS position time series. To model the time-varying part of the signal, a first-order autoregressive process is employed. The WF is then adapted to the noise level of the data to model only those time variabilities which are significant. Synthetic and real GPS data is used to demonstrate that this variation of the WF leaves the underlying noise properties intact and provides optimal modeling of seasonal signals. This methodology is referred to as the adaptive WF (AWF) and is both easy to implement and fast, due to the use of the fast Fourier transform method.
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subjects Autoregressive processes
Chemistry and Earth Sciences
Computer Science
Earth and Environmental Science
Earth Sciences
Fast Fourier transformations
Fourier transforms
Geodesy
Geotechnical Engineering & Applied Earth Sciences
Global positioning systems
GPS
Gravity
Hydrogeology
Modelling
Noise
Noise levels
Noise reduction
Physics
Positioning systems
Satellite navigation systems
Signal processing
Statistics for Engineering
Time series
Wiener filtering
title Noise-Dependent Adaption of the Wiener Filter for the GPS Position Time Series
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