Deterministic Sampling for Nonlinear Dynamic State Estimation

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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Bibliographic Details
Main Author: Gilitschenski, Igor (auth)
Format: Electronic Book Chapter
Language:English
Published: KIT Scientific Publishing 2016
Series:Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
Subjects:
Sensordatenfusion
Richtungsstatistik
Directional Statistics
Stochastische Filterung
Sensor Data Fusion
DichteapproximationStochastic Filtering
Density Approximation
Online Access:DOAB: download the publication
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Summary: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.
Physical Description:1 electronic resource (XVI, 167 p. p.)
ISBN:KSP/1000051670
9783731504733
Access:Open Access

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