01842nam 2200385z- 450 991034677440332120231214133131.01000051670(CKB)4920000000100779(oapen)https://directory.doabooks.org/handle/20.500.12854/44863(EXLCZ)99492000000010077920202102d2016 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierDeterministic Sampling for Nonlinear Dynamic State EstimationKIT Scientific Publishing20161 electronic resource (XVI, 167 p. p.)Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory3-7315-0473-1 The 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.SensordatenfusionRichtungsstatistikDirectional StatisticsStochastische FilterungSensor Data FusionDichteapproximationStochastic FilteringDensity ApproximationGilitschenski Igorauth1327888BOOK9910346774403321Deterministic Sampling for Nonlinear Dynamic State Estimation3038240UNINA