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210512 ||| eng |
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|a 1000051670
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|a 9783731504733
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|a Gilitschenski, Igor
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| 245 |
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|a Deterministic Sampling for Nonlinear Dynamic State Estimation
|h Elektronische Ressource
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| 260 |
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|b KIT Scientific Publishing
|c 2016
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| 300 |
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|a 1 electronic resource (XVI, 167 p. p.)
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| 653 |
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|a DichteapproximationStochastic Filtering
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| 653 |
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|a Density Approximation
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| 653 |
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|a Richtungsstatistik
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| 653 |
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|a Stochastische Filterung
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| 653 |
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|a Directional Statistics
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| 653 |
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|a Sensordatenfusion
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| 653 |
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|a Sensor Data Fusion
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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 Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
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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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| 024 |
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|a 10.5445/KSP/1000051670
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/44863
|z DOAB: description of the publication
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|u https://www.ksp.kit.edu/9783731504733
|7 0
|x Verlag
|3 Volltext
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| 520 |
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|a The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.
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