Deterministic Sampling for Nonlinear Dynamic State Estimation
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 distribut...
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| Autore principale: | |
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| Natura: | Elettronico Capitolo di libro |
| Lingua: | inglese |
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KIT Scientific Publishing
2016
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| Serie: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
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| Soggetti: | |
| Accesso online: | DOAB: download the publication DOAB: description of the publication |
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| Riassunto: | 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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| Descrizione fisica: | 1 electronic resource (XVI, 167 p. p.) |
| ISBN: | KSP/1000051670 9783731504733 |
| Accesso: | Open Access |