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Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

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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.

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Detail Information

Series Title

-

Call Number

510 HUB n

Publisher

: KIT Scientific Publishing., 2015

Collation

-

Language

English

ISBN/ISSN

978-3-7315-0338-5

Classification

510

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

-

Subject(s)

Computer Science

Specific Detail Info

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Statement of Responsibility

Marco Huber

Other Information

Cataloger

Kurnadi

Source

-

Validator

maya

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