Abstract
Nowadays, heterogeneous data sets are often combined within a parameter estimation process in order to benefit from their individual strengths and favorable features. Frequently, the different data sets are complementary with respect to their measurement principle, the accuracy, the spatial and temporal distribution and resolution, as well as their spectral characteristics. This paper gives first a review on various combination strategies based on the Gauss-Markov model; special attention will be turned on the stochastic modeling of the input data, e.g. the influence of correlations between different sets of input data. Furthermore, the method of variance component estimation is presented to determine the relative weighting between the observation techniques. If the input data sets are sensitive to different parts of the frequency spectrum the multi-scale representation might be applied which basically means the decomposition of a target function into a number of detail signals each related to a specific frequency band. A successive parameter estimation can be applied to determine the detail signals.
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Schmidt, M., Göttl, F., Heinkelmann, R. (2015). Towards the Combination of Data Sets from Various Observation Techniques. In: Kutterer, H., Seitz, F., Alkhatib, H., Schmidt, M. (eds) The 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems (QuGOMS'11). International Association of Geodesy Symposia, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-10828-5_6
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DOI: https://doi.org/10.1007/978-3-319-10828-5_6
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