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Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

Huber, Marco

出版	KIT Scientific Publishing, 2015-03-11
ISBN	37315033879783731503385
URL	http://books.google.com.hk/books?id=_aSrBwAAQBAJ&hl=&source=gbs_api
EBook	SAMPLE

註釋

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.