Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications
Huber, Marco
Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications - KIT Scientific Publishing 2015 - 1 electronic resource (V, 270 p. p.)
Open Access
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.
Creative Commons
English
KSP/1000045491 9783731503385
10.5445/KSP/1000045491 doi
Zustandsschätzung GauĂprozesseBayesian statistics Kalman filter Gaussian processes Kalman-Filter state estimation filtering Bayes'sche Statistik
Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications - KIT Scientific Publishing 2015 - 1 electronic resource (V, 270 p. p.)
Open Access
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.
Creative Commons
English
KSP/1000045491 9783731503385
10.5445/KSP/1000045491 doi
Zustandsschätzung GauĂprozesseBayesian statistics Kalman filter Gaussian processes Kalman-Filter state estimation filtering Bayes'sche Statistik