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Nonlinear state and parameter estimation of spatially distributed systems
Felix Sawo
2009
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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.
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Keywords
- distributed-parameter system
- nonlinear estimation
- sensor network
- sensor network, nonlinear estimation, distributed-parameter system
Links
DOI: 10.5445/KSP/1000011485Editions