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Marco Huber

Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

(Karlsruher Schriften zur Anthropomatik ; 19)

AutorHuber, Marco

VerlagKIT Scientific Publishing, Karlsruhe

ISBN9783731503385

UmfangV, 270 S.

Veröffentlicht
am:
11.03.2015

Erscheinungs-
jahr
2015

VerfügbarkeitAktiv

Downloads:

Für Zitate bitte die folgende URL verwenden:
http://dx.doi.org/10.5445/KSP/1000045491

Abstract

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.