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010   $a1000045491
035   $a(CKB)4920000000100687
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/54758
035   $a(EXLCZ)994920000000100687
100   $a20202102d2015      |y             0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aNonlinear Gaussian Filtering : Theory, Algorithms, and Applications
210   $cKIT Scientific Publishing$d2015
215   $a1 electronic resource (V, 270 p. p.)
225 1 $aKarlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe
311   $a3-7315-0338-7 
330   $aBy 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.
517   $aNonlinear Gaussian Filtering 
610   $aZustandsschätzung
610   $aGaußprozesseBayesian statistics
610   $aKalman filter
610   $aGaussian processes
610   $aKalman-Filter
610   $astate estimation
610   $afiltering
610   $aBayes'sche Statistik
700   $aHuber$b Marco$4auth$01302254
906   $aBOOK
912   $a9910346783603321
996   $aNonlinear Gaussian Filtering : Theory, Algorithms, and Applications$93031361
997   $aUNINA