TY - BOOK ID - 100226426 TI - Nonlinear state and parameter estimation of spatially distributed systems PY - 2009 SN - 1000011485 3866443706 PB - KIT Scientific Publishing DB - UniCat KW - sensor network KW - nonlinear estimation KW - distributed-parameter system UR - https://www.unicat.be/uniCat?func=search&query=sysid:100226426 AB - 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. ER -