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  • 1
    Online Resource
    Online Resource
    KIT Scientific Publishing
    UID:
    edocfu_9959145978402883
    Format: 1 electronic resource (V, 270 p. p.)
    ISBN: 1000045491
    Series Statement: Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe
    Content: By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems.
    Note: English
    Additional Edition: ISBN 3-7315-0338-7
    Language: English
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