Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications
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Beschreibung
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.
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.
Autor*in
Marco Huber
Themen in »Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications«
state estimation Kalman filter Zustandsschätzung Kalman-Filter Gaussian processes filtering Bayes'sche Statistik Gaußprozesse Bayesian statistics