Nonlinear state and parameter estimation of spatially distributed systems
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 id...
Saved in:
Main Author: | |
---|---|
Format: | Electronic Book Chapter |
Language: | English |
Published: |
KIT Scientific Publishing
2009
|
Series: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory
|
Subjects: | |
Online Access: | DOAB: download the publication DOAB: description of the publication |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | 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. |
---|---|
Physical Description: | 1 electronic resource (XI, 153 p. p.) |
ISBN: | KSP/1000011485 9783866443709 |
Access: | Open Access |