LEADER 01859nam  2200397z- 450 
001 9910346774403321
005 20210211
010   $a1000051670
035   $a(CKB)4920000000100779
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/44863
035   $a(oapen)doab44863
035   $a(EXLCZ)994920000000100779
100   $a20202102d2016      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aDeterministic Sampling for Nonlinear Dynamic State Estimation
210   $cKIT Scientific Publishing$d2016
215   $a1 online resource (XVI, 167 p. p.)
225 1 $aKarlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
311 08$a3-7315-0473-1 
330   $aThe goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.
610   $aDensity Approximation
610   $aDichteapproximationStochastic Filtering
610   $aDirectional Statistics
610   $aRichtungsstatistik
610   $aSensor Data Fusion
610   $aSensordatenfusion
610   $aStochastische Filterung
700   $aGilitschenski$b Igor$4auth$01327888
906   $aBOOK
912   $a9910346774403321
996   $aDeterministic Sampling for Nonlinear Dynamic State Estimation$93038240
997   $aUNINA