Title: Nonlinear state and parameter estimation of spatially distributed systems (Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universit�t Karlsruhe)
Publisher: KIT Scientific Publishing
Publication Date: 2009
Language: English
ISBN 10: 3866443706
ISBN 13: 9783866443709
Binding: Soft cover
Condition: New

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Synopsis

In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for identifying various model parameters. The Covariance Bounds Filter (CBF) allows the efficient estimation of widely distributed systems in a decentralized fashion.

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