UID:
almahu_9949711272002882
Format:
1 electronic resource (XI, 153 p. p.)
ISBN:
1000011485
Series Statement:
Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory
Content:
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.
Note:
English
Additional Edition:
ISBN 3-86644-370-6
Language:
English
