Nonlinear Gaussian Filtering: Theory, Algorithms, and Applications

By Marco Huber

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.

This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

• Format: Hardcover
• Language: English
• ISBN: 1013280695
• ISBN13: 9781013280696
• Release Date: October 2020
• Publisher: Saint Philip Street Press
• Length: 296 Pages
• Weight: 2.15 lbs.
• Dimensions: 0.7" x 8.5" x 11.0"