Deterministic Sampling for Nonlinear Dynamic State Estimation

• Autore: Gilitschenski Igor
• Pubblicazione: KIT Scientific Publishing, 2016
• Descrizione fisica: 1 electronic resource (XVI, 167 p. p.)
• Soggetto non controllato: Sensordatenfusion; Richtungsstatistik; Directional Statistics; Stochastische Filterung; Sensor Data Fusion; DichteapproximationStochastic Filtering; Density Approximation
• Sommario/riassunto: 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.
• Titolo autorizzato: Deterministic Sampling for Nonlinear Dynamic State Estimation
• ISBN: 1000051670
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910346774403321
• Lo trovi qui: Univ. Federico II