Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

by Marco Huber

Institution: Universität Karlsruhe
Year: 2015
Keywords: Bayes'sche Statistik, Zustandsschätzung, Kalman-Filter, Gaußprozesse Bayesian statistics, state estimation, filtering, Kalman filter, Gaussian processes
Record ID: 1099495
Full text PDF: http://digbib.ubka.uni-karlsruhe.de/volltexte/documents/3436059


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.