Noise in DORIS station position time series provided by IGN-JPL, INASAN and CNES-CLS Analysis Centres for the ITRF2014 realization

Khelifa, Sofiane

doi:10.1016/j.asr.2016.06.004

Cited by 6 publications

(6 citation statements)

References 49 publications

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“…This period is related to the revolution of the TOPEX/Poseidon, Jason-1 and Jason-2 satellites' orbits (− 3.0679 • /day). Similar results were reported for the DORIS time series by Le Bail (2006), Khelifa (2016), Bloßfeld et al (2016) and Tornatore et al (2016). The nodal period for the remaining satellites is nearly equal to the annual period which causes draconitic periods to tend to infinity .…”

Section: Seasonal Oscillationssupporting

confidence: 74%

“…In other words, since it distorts spectral indices causing their shift towards white noise, wavelet decomposition is too broad to be applied prior to a noise analysis. Nevertheless, wavelet decomposition can still be employed to investigate the longterm nonlinear trend (Bogusz 2015;Khelifa 2016), but it is a type of wavelet which has to be carefully chosen. Beyond wavelet decomposition, the SSA approach has also been previously applied to the GPS (e.g.…”

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

“…Bessissi et al (2009) applied wavelet decomposition (WD) with a Meyer wavelet to deliver the amplitudes of seasonal signals, nonlinear long-term signals and noise level in the DORIS data. Khelifa (2016) applied wavelet decomposition and Allan variance, one by one, to deliver the parameters of deterministic and stochastic parts. Wavelet decomposition is affected by the artificial loss of power at the considered (e.g.…”

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

“…The nonlinearities, previously described as present in DORIS data by Bessissi et al (2009) or Khelifa (2016), were modelled with polynomials of up to fourth degree. We also tested polynomials of higher degrees and proved that their coefficients are insignificant.…”

Section: Nonlinear Signalsmentioning

confidence: 99%

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Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

Kłos

Bogusz

Moreaux

2017

J Geod

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This paper focuses on the investigation of the deterministic and stochastic parts of the Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) weekly time series aligned to the newest release of ITRF2014. A set of 90 stations was divided into three groups depending on when the data were collected at an individual station. To reliably describe the DORIS time series, we employed a mathematical model that included the long-term nonlinear signal, linear trend, seasonal oscillations and a stochastic part, all being estimated with maximum likelihood estimation. We proved that the values of the parameters delivered for DORIS data are strictly correlated with the time span of the observations. The quality of the most recent data has significantly improved. Not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. Among several tested models, the power-law process may be chosen as the preferred one for most of the DORIS data. Moreover, the preferred noise model has changed through the years from an autoregressive process to pure power-law noise with few stations characterised by a positive spectral index. For the latest observations, the medians of the velocity errors were equal to 0.3, 0.3 and 0.4 mm/year, respectively, for the North, East and Up components. In the best cases, a velocity uncertainty of DORIS sites of 0.1 mm/year is achievable when the appropriate coloured noise model is taken into consideration.

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Section: Seasonal Oscillationssupporting

confidence: 74%

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

Section: Nonlinear Signalsmentioning

confidence: 99%

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Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

Kłos

Bogusz

Moreaux

2017

J Geod

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“…An AVAR analysis of the noise characteristics in the time series for DORIS station position was presented in [19]. Once the trends and seasonal components from the analyzed time series were eliminated, the results of the subsequent AVAR analysis indicated that the three solutions are dominated by white noise in all three components (north, east, and vertical).…”

Section: Geodesymentioning

confidence: 99%

Application of the Allan Variance to Time Series Analysis in Astrometry and Geodesy: A Review

Malkin

2016

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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The Allan variance (AVAR) was introduced 50 years ago as a statistical tool for assessing of the frequency standards deviations. For the past decades, AVAR has increasingly being used in geodesy and astrometry to assess the noise characteristics in geodetic and astrometric time series. A specific feature of astrometric and geodetic measurements, as compared with the clock measurements, is that they are generally associated with uncertainties; thus, an appropriate weighting should be applied during data analysis. Besides, some physically connected scalar time series naturally form series of multidimensional vectors. For example, three station coordinates time series X, Y , and Z can be combined to analyze 3D station position variations. The classical AVAR is not intended for processing unevenly weighted and/or multidimensional data. Therefore, AVAR modifications, namely weighted AVAR (WAVAR), multidimensional AVAR (MAVAR), and weighted multi-dimensional AVAR (WMAVAR), were introduced to overcome these deficiencies. In this paper, a brief review is given of the experience of using AVAR and its modifications in processing astro-geodetic time series.

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

et al. 2018

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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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Noise in DORIS station position time series provided by IGN-JPL, INASAN and CNES-CLS Analysis Centres for the ITRF2014 realization

Cited by 6 publications

References 49 publications

Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

Application of the Allan Variance to Time Series Analysis in Astrometry and Geodesy: A Review

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

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