Nonlinear state and parameter estimation of spatially distributed systems

By Felix Sawo

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.

• Format: Paperback
• Language: English
• ISBN: 3866443706
• ISBN13: 9783866443709
• Release Date: May 2009
• Publisher: Karlsruher Institut Fur Technologie
• Length: 176 Pages
• Weight: 0.48 lbs.
• Dimensions: 0.4" x 5.8" x 8.3"