Application of Kalman filtering in VLBI data analysis

Nilsson, Tobias; Soja, Benedikt; Karbon, Maria; Heinkelmann, Robert; Schuh, Harald

doi:10.1186/s40623-015-0307-y

Cited by 45 publications

(16 citation statements)

References 16 publications

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“…In this research we have developed a new empirical FCN model with higher temporal resolution by fitting M a n u s c r i p t the amplitude parameters directly to the Very Long Baseline Interferometry (VLBI) solution calculated with the GeoForschungsZentrum (GFZ) version (Nilsson et al, 2015) of the Vienna VLBI Software (VieVS) (Böhm et al, 2012). A comparison with other recently determined empirical FCN models: Malkin (2013), Krásná et al (2013) and Lambert and Dehant (2007) was included by means of the weight root mean square (WRMS) of the residuals during the entire period of VLBI data.…”

Section: Introductionmentioning

confidence: 99%

Testing a new Free Core Nutation empirical model

Belda

Ferrándiz

Heinkelmann

et al. 2016

Journal of Geodynamics

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The Free Core Nutation (FCN) is a free mode of the Earth's rotation caused by the different material characteristics of the Earth's core and mantle. This causes the rotational axes of those layers to slightly diverge from each other, resulting in a wobble of the Earth's rotation axis comparable to nutations. In this paper we focus on estimating empirical FCN models using the observed nutations derived from the VLBI sessions between 1993 and 2013. Assuming a fixed value for the oscillation period, the time-variable amplitudes and phases are estimated by means of multiple sliding window analyses. The effects of using different a priori Earth Rotation Parameters (ERP) in the derivation of models are also addressed. The optimal choice of the fundamental parameters of the model, namely the window width and step-size of its shift, is searched by performing a thorough experimental analysis using real data. The former analyses lead to the derivation of a model with a temporal resolution higher than the one used in the models currently available, with a sliding window reduced to 400 days and a day by day shift. It is shown that this new model increases the accuracy of the modeling of the observed Earth's rotation. Besides, empirical models determined from USNO Finals as a priori ERP present a slightly lower Weighted Root Mean Square (WRMS) of residuals than IERS 08 C04 along the whole period of VLBI observations, according to our computations. The model is also validated through comparisons with other recognized models. The level of agreement among them is satisfactory. Let us remark that our estimates give rise to the lowest residuals and seem to reproduce the FCN signal in more detail.

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Section: Introductionmentioning

confidence: 99%

Testing a new Free Core Nutation empirical model

Belda

Ferrándiz

Heinkelmann

et al. 2016

Journal of Geodynamics

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“…A Kalman filter approach has been successfully used by the ITRS combination center at NASA Jet Propulsion Laboratory (JPL) for their JTRF2008 solution, providing station coordinates at weekly intervals together with secular velocities and seasonal signals . For the analysis of very long baseline interferometry (VLBI, Schuh and Behrend, 2012;Schuh and Böhm, 2013) observations, Kalman filtering was introduced by Herring et al (1990), and recently applied in studies by Nilsson et al (2015), Soja et al (2015), and Karbon et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Determination of a terrestrial reference frame via Kalman filtering of very long baseline interferometry data

Soja

Nilsson

Balidakis

et al. 2016

J Geod

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Terrestrial reference frames (TRF), such as the ITRF2008, are primary products of geodesy. In this paper, we present TRF solutions based on Kalman filtering of very long baseline interferometry (VLBI) data, for which we estimate steady station coordinates over more than 30 years that are updated for every single VLBI session. By applying different levels of process noise, non-linear signals, such as seasonal and seismic effects, are taken into account. The corresponding stochastic model is derived site-dependent from geophysical loading deformation time series and is adapted during periods of post-seismic deformations. Our results demonstrate that the choice of stochastic process has a much smaller impact on the coordinate time series and velocities than the overall noise level. If process noise is applied, tests with and without additionally estimating seasonal signals indicate no difference between the resulting coordinate time series for periods when observational data are available. In a comparison with epoch reference frames, the Kalman filter solutions provide better short-term stability. Furthermore, we find out that the Kalman filter solutions are of similar quality when compared to a consistent least-squares solution, however, with the enhanced attribute of being

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“…As a result, the datum conditions are fulfilled at the level of 0.01 mm for translations and 1 μas for rotations at every epoch. More details about the Kalman filter implementation can be found in Nilsson et al (2015).…”

Section: Methodsmentioning

confidence: 99%

“…Widely used measures to quantify the effects on station positions are station coordinate and baseline length repeatabilities (Davis et al 1985). Nilsson et al (2015) showed that by using a Kalman filter instead of least squares adjustment, an improvement in the baseline length repeatabilities of about 10 % is possible. Here, we wanted to focus on the effect of a station-specific noise model for ZWD.…”

Section: Effects On Station Coordinatesmentioning

confidence: 99%

Tropospheric delay determination by Kalman filtering VLBI data

et al. 2015

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The troposphere is one of the most important error sources for space geodetic techniques relying on radio signals. Since it is not possible to model the wet part of the tropospheric delay with sufficient accuracy, it needs to be estimated from the observational data. In the analysis of very long baseline interferometry (VLBI) data, the parameter estimation is routinely performed using a least squares adjustment. In this paper, we investigate the application of a Kalman filter for parameter estimation, specifically focusing on the tropospheric delays. The main advantages of a Kalman filter are its real-time capability and stochastic approach. We focused on the latter and derived stochastic models for VLBI zenith wet delays, taking into account temporal and location-based differences. Compared to a static noise model, the quality of station coordinates, also estimated in the Kalman filter, increased as a result. In terms of baseline length and station coordinate repeatabilities, this improvement amounted to 2.3 %. Additionally, we compared the Kalman filter and least squares results for VLBI with zenith wet delays derived from GPS (Global Positioning System), water vapor radiometers, and ray tracing in numerical weather models. The agreement of the Kalman filter VLBI solution with respect to water vapor radiometer data was larger than that of the least squares solution by 6-15 %. Our investigations are based on selected VLBI data (CONT campaigns) that are closest to how future VLBI infrastructure is designed to operate. With the aim for continuous and near real-time parameter estimation and the promising results which we have achieved in this study, we expect Kalman filtering to grow in importance in VLBI analysis.

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Application of Kalman filtering in VLBI data analysis

Cited by 45 publications

References 16 publications

Testing a new Free Core Nutation empirical model

Testing a new Free Core Nutation empirical model

Determination of a terrestrial reference frame via Kalman filtering of very long baseline interferometry data

Tropospheric delay determination by Kalman filtering VLBI data

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