Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture f...

Full description

Saved in:
Bibliographic Details
Main Author: Huber, Marco (auth)
Format: Electronic Book Chapter
Language:English
Published: KIT Scientific Publishing 2015
Series:Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe
Subjects:
Zustandsschätzung
GaußprozesseBayesian statistics
Kalman filter
Gaussian processes
Kalman-Filter
state estimation
filtering
Bayes'sche Statistik
Online Access:DOAB: download the publication
DOAB: description of the publication
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems.
Physical Description:1 electronic resource (V, 270 p. p.)
ISBN:KSP/1000045491
9783731503385
Access:Open Access

Similar Items