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Deterministic Sampling for Nonlinear Dynamic State Estimation
dc.creator | Gilitschenski, Igor | |
dc.date.accessioned | 2021-02-23T14:01:00Z | |
dc.date.available | 2021-02-23T14:01:00Z | |
dc.date.created | 2016 | |
dc.identifier.isbn | 9783731504733 | |
dc.identifier.other | https://directory.doabooks.org/handle/20.500.12854/44863 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12010/17645 | |
dc.description.abstract | 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. | spa |
dc.format.extent | XVI, 167 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.publisher | KIT Scientific Publishing | spa |
dc.subject | Sensordatenfusion | spa |
dc.subject | Richtungsstatistik | spa |
dc.subject | Directional Statistics | spa |
dc.title | Deterministic Sampling for Nonlinear Dynamic State Estimation | spa |
dc.subject.lemb | Computación sensible al contexto | spa |
dc.subject.lemb | Redes de sensores | spa |
dc.subject.lemb | Redes de sensores inalámbricos | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.local | Abierto (Texto Completo) | spa |
dc.identifier.doi | 10.5445/KSP/1000051670 | |
dc.type.coar | http://purl.org/coar/resource_type/c_2f33 | spa |
dc.rights.creativecommons | https://creativecommons.org/licenses/by-sa/4.0/ |