01957nam 2200421z- 450 9910346783603321202102111000045491(CKB)4920000000100687(oapen)https://directory.doabooks.org/handle/20.500.12854/54758(oapen)doab54758(EXLCZ)99492000000010068720202102d2015 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierNonlinear Gaussian Filtering : Theory, Algorithms, and ApplicationsKIT Scientific Publishing20151 online resource (V, 270 p. p.)Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe3-7315-0338-7 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.Nonlinear Gaussian Filtering Bayes'sche StatistikfilteringGaussian processesGaußprozesseBayesian statisticsKalman filterKalman-Filterstate estimationZustandsschätzungHuber Marcoauth1302254BOOK9910346783603321Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications3031361UNINA