Title: Nonlinear state and parameter estimation of spatially distributed systems
Publisher: Karlsruher Institut Fur Technologie
Publication Date: 2014
Language: English
ISBN 10: 3866443706
ISBN 13: 9783866443709
Binding: Paperback
Condition: New
Dimensions: 14.8 centimeters width by 1.04 centimeters height by 21 centimeters depth
Weight: 218 grams

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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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