Marco Huber Bücher

The thesis explores the optimal Bayesian filtering problem by focusing on Gaussian distributions, enabling the development of computationally efficient algorithms. It addresses three specific scenarios: filtering using only Gaussian distributions, employing Gaussian mixture filtering for handling strong nonlinearities, and utilizing Gaussian process filtering in data-driven contexts. For each scenario, the author derives effective algorithms and demonstrates their application to real-world challenges, highlighting the practical implications of these methods in various domains.

A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions.