Part of
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.
Umfang: XVI, 167 S.
Preis: €41.00 | £38.00 | $72.00
These are words or phrases in the text that have been
automatically identified by the
Named Entity Recognition and Disambiguation service,
which provides Wikipedia
(
)
and Wikidata
(
)
links for these entities.
Gilitschenski, I. 2016. Deterministic Sampling for Nonlinear Dynamic State Estimation. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000051670
Dieses Buch ist lizenziert unter Creative Commons Attribution + ShareAlike 3.0 DE Dedication
Dieses Buch ist Peer reviewed. Informationen dazu finden Sie hier
Veröffentlicht am 19. April 2016
Englisch
200
| Paperback | 978-3-7315-0473-3 |