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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.
Umfang: XI, 153 S.
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Sawo, F. 2009. Nonlinear state and parameter estimation of spatially distributed systems. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000011485
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Veröffentlicht am 26. Mai 2009
Englisch
180
| Paperback | 978-3-86644-370-9 |