Nonlinear state and parameter estimation of spatially distributed systems
Nonlinear state and parameter estimation of spatially distributed systems By Sawo F.
2009 | 356 Pages | ISBN: 3866443706 | PDF | 9 MB
2009 | 356 Pages | ISBN: 3866443706 | PDF | 9 MB
In this book 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.