TY  - GEN
AU  - Gilitschenski,Igor
TI  - Deterministic Sampling for Nonlinear Dynamic State Estimation
SN  - KSP/1000051670
PY  - 2016///
PB  - KIT Scientific Publishing
KW  - Sensordatenfusion
KW  - Richtungsstatistik
KW  - Directional Statistics
KW  - Stochastische Filterung
KW  - Sensor Data Fusion
KW  - DichteapproximationStochastic Filtering
KW  - Density Approximation
N1  - Open Access
N2  - 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
UR  - https://www.ksp.kit.edu/9783731504733
UR  - https://directory.doabooks.org/handle/20.500.12854/44863
ER  -