01658nam 2200349z- 450 9910346924403321202102111000011485(CKB)4920000000101278(oapen)https://directory.doabooks.org/handle/20.500.12854/54761(oapen)doab54761(EXLCZ)99492000000010127820202102d2009 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierNonlinear state and parameter estimation of spatially distributed systemsKIT Scientific Publishing20091 online resource (XI, 153 p. p.)Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory3-86644-370-6 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.distributed-parameter systemnonlinear estimationsensor networkSawo Felixauth1328622BOOK9910346924403321Nonlinear state and parameter estimation of spatially distributed systems3038739UNINA