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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| Autor principal: | |
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| Formato: | Online |
| Lenguaje: | inglés |
| Publicado: |
KIT Scientific Publishing
2021
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| Materias: | |
| Acceso en línea: | 35078 |
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| _version_ | 1869523039786369024 |
|---|---|
| author | Gilitschenski, Igor |
| author_browse | Gilitschenski, Igor |
| author_facet | Gilitschenski, Igor |
| author_sort | Gilitschenski, Igor |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-44863 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-448632023-12-20T18:40:47Z Deterministic Sampling for Nonlinear Dynamic State Estimation Gilitschenski, Igor QA75.5-76.95 Sensordatenfusion Richtungsstatistik Directional Statistics Stochastische Filterung Sensor Data Fusion DichteapproximationStochastic Filtering Density Approximation bic Book Industry Communication::U Computing & information technology::UY Computer science 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. 2021-02-11T11:14:41Z 2021-02-11T11:14:41Z 2019-07-30 20:02:00 2016 book 35078 18673813 9783731504733 https://directory.doabooks.org/handle/20.500.12854/44863 eng Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory image/jpeg Attribution-ShareAlike 4.0 International https://www.ksp.kit.edu/9783731504733 KIT Scientific Publishing 10.5445/KSP/1000051670 10.5445/KSP/1000051670 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731504733 XVI, 167 p. open access |
| spellingShingle | QA75.5-76.95 Sensordatenfusion Richtungsstatistik Directional Statistics Stochastische Filterung Sensor Data Fusion DichteapproximationStochastic Filtering Density Approximation bic Book Industry Communication::U Computing & information technology::UY Computer science Gilitschenski, Igor Deterministic Sampling for Nonlinear Dynamic State Estimation |
| title | Deterministic Sampling for Nonlinear Dynamic State Estimation |
| title_full | Deterministic Sampling for Nonlinear Dynamic State Estimation |
| title_fullStr | Deterministic Sampling for Nonlinear Dynamic State Estimation |
| title_full_unstemmed | Deterministic Sampling for Nonlinear Dynamic State Estimation |
| title_short | Deterministic Sampling for Nonlinear Dynamic State Estimation |
| title_sort | deterministic sampling for nonlinear dynamic state estimation |
| topic | QA75.5-76.95 Sensordatenfusion Richtungsstatistik Directional Statistics Stochastische Filterung Sensor Data Fusion DichteapproximationStochastic Filtering Density Approximation bic Book Industry Communication::U Computing & information technology::UY Computer science |
| topic_facet | QA75.5-76.95 Sensordatenfusion Richtungsstatistik Directional Statistics Stochastische Filterung Sensor Data Fusion DichteapproximationStochastic Filtering Density Approximation bic Book Industry Communication::U Computing & information technology::UY Computer science |
| url | 35078 |
| work_keys_str_mv | AT gilitschenskiigor deterministicsamplingfornonlineardynamicstateestimation |