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151-0566-00L
Recursive Estimation

Professor(en):
R. D' Andrea
Betreuer:
Vorlesung:
Link zum Kurskatalog
Spring 2018
Webseite:
Ziele:
Recursive methods for real-time filtering and estimation. Topics covered: basics of probability, state space representation of dynamic systems, Bayes theorem, recursive least-squares, Kalman filtering, particle filters, probabilistic data fusion, and multi-sensor estimation. The attendees will become familiar with practical methods for real-time filtering and estimation. The material will cover the theory of the selected methods and show numerous application examples. Homework assignments will vary from theoretical aspects to computer exercises using Matlab for the actual design of some filters and estimators.
Vorlesungslevel:
D-ITET Master, Systems and Control specialization
Recommended Core Courses
Voraussetzungen:
Inhalt:
Inhalt basic probability, Bayes theorem, recursive least squares, linear Kalman filter, non-linear Kalman filters, Bayes filtering, particle filters, probabilistic data fusion, multi-sensor estimation http://www.vvz.ethz.ch/Vorlesungsverzeichnis/lerneinheitPre.do?lerneinheitId=60257&semkez=2009S&lang=de
Dokumentation: