Inhalte:
Stochastic Systems: Stochastic processes, noise, processes with independent increments, Brownian motion, Wiener Processes,continuity and differentialability of stochastic processes, white noise, modeling with additive noise processes, integration of stochastic processes, Wiener's stochastic integral, Markovian processes, Gaussian-Markovian processes, linear systems with white noise Gaussian input processes, Wiener-Lee theory
Estimation Approaches: Bayesian estimation, conditional mean, maximum a-posteriori, maximum likelihood estimation, minimum variance and weighted least squares estimation, maximum entropy approaches
Optimal filtering: Kalman filter as a recursive algorithm, covariance cycle and inverse covariance cycle, Fisher's information, orthogonal projections, innovations approach, information filter
LERNZIELE:
Provide a profound basement of stochastic modeling and optimal filtering
FAKTENWISSEN:
Optimal processing of noisy measurements based on stochastic modeling of sensors and plants, Kalman filter and Kalman smoother design
METHODENKOMPETENZ:
Probability theory, theory of stochastic processes and optimal filtering
LERNZIELELITERATUR:
W.B. Davenport, Probability and Random Variables, Mc Graw-Hill,
P.S. Maybeck, Stochastic Models Estimation and Control, Academic Press
B.D.O. Anderson, J.B. More, Optimal Filtering, Prentice Hall
O. Loffeld; Estimationstheorie I, II, Oldenbourg Verlag, in Bibliothek vorhanden
PRUEFUNGSFORM: oral examinations offered on request
Stochastic Systems: Stochastic processes, noise, processes with independent increments, Brownian motion, Wiener Processes,continuity and differentialability of stochastic processes, white noise, modeling with additive noise processes, integration of stochastic processes, Wiener's stochastic integral, Markovian processes, Gaussian-Markovian processes, linear systems with white noise Gaussian input processes, Wiener-Lee theory
Estimation Approaches: Bayesian estimation, conditional mean, maximum a-posteriori, maximum likelihood estimation, minimum variance and weighted least squares estimation, maximum entropy approaches
Optimal filtering: Kalman filter as a recursive algorithm, covariance cycle and inverse covariance cycle, Fisher's information, orthogonal projections, innovations approach, information filter
LERNZIELE:
Provide a profound basement of stochastic modeling and optimal filtering
FAKTENWISSEN:
Optimal processing of noisy measurements based on stochastic modeling of sensors and plants, Kalman filter and Kalman smoother design
METHODENKOMPETENZ:
Probability theory, theory of stochastic processes and optimal filtering
LERNZIELELITERATUR:
W.B. Davenport, Probability and Random Variables, Mc Graw-Hill,
P.S. Maybeck, Stochastic Models Estimation and Control, Academic Press
B.D.O. Anderson, J.B. More, Optimal Filtering, Prentice Hall
O. Loffeld; Estimationstheorie I, II, Oldenbourg Verlag, in Bibliothek vorhanden
PRUEFUNGSFORM: oral examinations offered on request
- Dozent/in: Otmar Loffeld