LEADER 01658nam  2200349z- 450 
001 9910346924403321
005 20210211
010   $a1000011485
035   $a(CKB)4920000000101278
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/54761
035   $a(oapen)doab54761
035   $a(EXLCZ)994920000000101278
100   $a20202102d2009      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aNonlinear state and parameter estimation of spatially distributed systems
210   $cKIT Scientific Publishing$d2009
215   $a1 online resource (XI, 153 p. p.)
225 1 $aKarlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory
311 08$a3-86644-370-6 
330   $aIn 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.
610   $adistributed-parameter system
610   $anonlinear estimation
610   $asensor network
700   $aSawo$b Felix$4auth$01328622
906   $aBOOK
912   $a9910346924403321
996   $aNonlinear state and parameter estimation of spatially distributed systems$93038739
997   $aUNINA