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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| Superior document: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory |
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| Year of Publication: | 2009 |
| Language: | English |
| Series: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory
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| Physical Description: | 1 electronic resource (XI, 153 p. p.) |
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| 100 | 1 | |a Sawo, Felix |4 auth | |
| 245 | 1 | 0 | |a Nonlinear state and parameter estimation of spatially distributed systems |
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| 490 | 1 | |a Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory | |
| 520 | |a 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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| 653 | |a sensor network | ||
| 653 | |a nonlinear estimation | ||
| 653 | |a distributed-parameter system | ||
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