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210512 ||| eng |
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|a 9783731503385
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|a 1000045491
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|a Huber, Marco
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|a Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications
|h Elektronische Ressource
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| 260 |
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|b KIT Scientific Publishing
|c 2015
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| 300 |
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|a 1 electronic resource (V, 270 p. p.)
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| 653 |
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|a Kalman-Filter
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| 653 |
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|a GaußprozesseBayesian statistics
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| 653 |
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|a filtering
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| 653 |
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|a Kalman filter
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| 653 |
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|a Gaussian processes
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| 653 |
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|a Bayes'sche Statistik
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| 653 |
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|a state estimation
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| 653 |
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|a Zustandsschätzung
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 490 |
0 |
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|a Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-sa/4.0/
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|a 10.5445/KSP/1000045491
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|u https://directory.doabooks.org/handle/20.500.12854/54758
|z DOAB: description of the publication
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|u https://www.ksp.kit.edu/9783731503385
|7 0
|x Verlag
|3 Volltext
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| 520 |
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|a 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.
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