TY  - BOOK
AU  - Sawo, Felix
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.
DO  - 10.5445/KSP/1000011485
ID  - OAPEN ID: 34479
KW  - sensor network
KW  - nonlinear estimation
KW  - distributed-parameter system
L1  - https://www.ksp.kit.edu/9783866443709
LA  - English
LK  - https://directory.doabooks.org/handle/20.500.12854/54761
PB  - KIT Scientific Publishing
PY  - 2009
SN  - 9783866443709
TI  - Nonlinear state and parameter estimation of spatially distributed systemsnull
ER  - 
