Nonlinear state and parameter estimation of spatially distributed systems - Dissertationsschrift

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.

Product details

• Authors: Felix Sawo
• Publisher: KIT Scientific Publishing
• Languages: English
• Product format: Paperback / Softback
• Released: 06.07.2009
• EAN: 9783866443709
• ISBN: 978-3-86644-370-9
• No. of pages: 153
• Weight: 300 g
• Illustrations: Ill., graph. Darst.
• Series: Karlsruhe Series on Intelligent Sensor-Actuator-Systems; Karlsruhe Series on Intelligent Sensor-Actuator-Systems
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT