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[null,"1.","Record Nr.",null,"UNINA9910346924403321"]
[null,null,"Autore",null,"Sawo Felix"]
[null,null,"Titolo",null,"Nonlinear state and parameter estimation of spatially distributed systems"]
[null,null,"Pubbl/distr/stampa",null,"KIT Scientific Publishing, 2009"]
[null,null,"ISBN",null,"1000011485"]
[null,null,"Descrizione fisica",null,"1 online resource (XI, 153 p. p.)"]
[null,null,"Collana",null,"Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","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."]