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...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
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KIT Scientific Publishing
2009
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| Colección: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory
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| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009424832706719 |
| Sumario: | 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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| Descripción Física: | 1 electronic resource (XI, 153 p. p.) |
