Work

Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

Year: 2015

Type: dissertation

Abstract: 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.

Source: Repository KITopen (Karlsruhe Institute of Technology)

DOI: 10.5445/ir/1000046060

Author: Huber, Marco

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Deutsche Forschungsgemeinschaft, Multidisciplinary University Research Initiative, Office of Naval Research

Award: G937060653

OpenAlex ID: W2530847783

Cites:

Cited by: 14

Related to: 20