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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| Main Author: | |
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| Format: | Electronic Book Chapter |
| Language: | English |
| Published: |
KIT Scientific Publishing
2016
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| Series: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
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| Subjects: | |
| Online Access: | DOAB: download the publication DOAB: description of the publication |
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| 005 | 20210211 | ||
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| 008 | 20210211s2016 xx |||||o ||| 0|eng d | ||
| 020 | |a KSP/1000051670 | ||
| 020 | |a 9783731504733 | ||
| 040 | |a oapen |c oapen | ||
| 024 | 7 | |a 10.5445/KSP/1000051670 |c doi | |
| 041 | 0 | |a eng | |
| 042 | |a dc | ||
| 100 | 1 | |a Gilitschenski, Igor |4 auth | |
| 245 | 1 | 0 | |a Deterministic Sampling for Nonlinear Dynamic State Estimation |
| 260 | |b KIT Scientific Publishing |c 2016 | ||
| 300 | |a 1 electronic resource (XVI, 167 p. p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory | |
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |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. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-sa/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-sa/4.0/ | ||
| 546 | |a English | ||
| 653 | |a Sensordatenfusion | ||
| 653 | |a Richtungsstatistik | ||
| 653 | |a Directional Statistics | ||
| 653 | |a Stochastische Filterung | ||
| 653 | |a Sensor Data Fusion | ||
| 653 | |a DichteapproximationStochastic Filtering | ||
| 653 | |a Density Approximation | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://www.ksp.kit.edu/9783731504733 |7 0 |z DOAB: download the publication |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/44863 |7 0 |z DOAB: description of the publication |