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
Format: eBook
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:
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
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245 0 0 |a Deterministic Sampling for Nonlinear Dynamic State Estimation  |h Elektronische Ressource 
260 |b KIT Scientific Publishing  |c 2016 
300 |a 1 electronic resource (XVI, 167 p. p.) 
653 |a DichteapproximationStochastic Filtering 
653 |a Density Approximation 
653 |a Richtungsstatistik 
653 |a Stochastische Filterung 
653 |a Directional Statistics 
653 |a Sensordatenfusion 
653 |a Sensor Data Fusion 
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490 0 |a Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory 
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856 4 0 |u https://www.ksp.kit.edu/9783731504733  |x Verlag  |3 Volltext 
520 |a 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.