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A celestial reference frame based on Kalman filtering

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dc.contributor.author Soja, B.
dc.contributor.author Gross, R.
dc.contributor.author Jacobs, C.
dc.contributor.author Chin, T.
dc.contributor.author Abbondanza, C.
dc.contributor.author Heflin, M.
dc.contributor.author Parker, J.
dc.contributor.author Wu, X.
dc.contributor.author Karbon, M.
dc.contributor.author Nilsson, T.
dc.contributor.author Heinkelmann, R.
dc.contributor.author Schuh, H.
dc.date.accessioned 2019-07-02T18:21:19Z
dc.date.available 2019-07-02T18:21:19Z
dc.date.issued 2017-05-15
dc.identifier.citation 23rd Working Meeting of the European VLBI Group for Geodesy and Astrometry (EVGA), Gothenburg, Sweden, May 15-19, 2017 en_US
dc.identifier.clearanceno CL#17-4757
dc.identifier.uri http://hdl.handle.net/2014/46391
dc.description.abstract In this study, we investigate a novel approach to the determination of celestial reference frames (CRF). Instead of a constant model for radio sources positions, we adopt a time series representation, which allows temporal variations of radio source coordinates to be taken into account. In particular, the added flexibility is beneficial for radio sources with extended structure. We compute our time series-based CRF solutions by Kalman filtering and smoothing radio source positions, which are initially obtained from single-session VLBI analysis. The temporal resolution of the estimated CRF coordinates is identical to that of the input data, i.e. usually 1-4 days. The magnitude of the coordinate variations is controlled by the amount of process noise applied in the filter, which is in turn derived from analyzing the Allan standard deviation of the corresponding radio source coordinate time series. Measures have been developed to reduce the impact of observation errors and datum effects on the noise model. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2017 en_US
dc.title A celestial reference frame based on Kalman filtering en_US
dc.type Preprint en_US


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