Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

Know about the book -

Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications is written by Huber, Marco and published by KIT Scientific Publishing. It's available with International Standard Book Number or ISBN identification 3731503387 (ISBN 10) and 9783731503385 (ISBN 13).

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.