Deterministic Sampling for Nonlinear Dynamic State Estimation - Igor Gilitschenski
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Toimitus 15-21 arkipäivässä
30 päivän palautusoikeus
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 n ... Täydellinen kuvaus
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Kuvaus
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.
Lisätietoja
| Kirjoittaja | Igor Gilitschenski |
|---|---|
| Julkaisija | Karlsruher Institut für Technologie |
| Julkaisuvuosi | 2016 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9783731504733 |