TY  - GEN
AU  - Huber,Marco
TI  - Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications
SN  - KSP/1000045491
PY  - 2015///
PB  - KIT Scientific Publishing
KW  - Zustandsschätzung
KW  - GaußprozesseBayesian statistics
KW  - Kalman filter
KW  - Gaussian processes
KW  - Kalman-Filter
KW  - state estimation
KW  - filtering
KW  - Bayes'sche Statistik
N1  - Open Access
N2  - 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
UR  - https://www.ksp.kit.edu/9783731503385
UR  - https://directory.doabooks.org/handle/20.500.12854/54758
ER  -