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URN: urn:nbn:de:swb:90-460607

Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

Huber, Marco

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.


Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Hochschulschrift
Jahr 2015
Sprache Englisch
Identifikator KITopen ID: 1000046060
Verlag KIT, Karlsruhe
Umfang XVI, 551 S.
Abschlussart Habilitation
Bemerkung zur Veröffentlichung Eingereicht am: 14.02.2014
URLs gekürzte Druckfassung
Schlagworte Bayes'sche Statistik, Zustandsschätzung, Kalman-Filter, Gaußprozesse Bayesian statistics, state estimation, filtering, Kalman filter, Gaussian processes
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