Estimation and Filtering

B232 - Summer 23/24
You don't have access to the course
B181 - Winter 18/19

Estimation and Filtering - XP35OFD

Student survey results
Credits 4
Semesters Winter
Completion Exam
Language of teaching undefined
Extent of teaching 2P+2C
Annotation
Methodology: experiment design, structure selection and parameter estimation. Bayesian approach to uncertainty description. Posterior probability density function and point estimates: MS, LMS, ML and MAP. Robust numerical implementation of least squares estimation for Gaussian distribution. Parameter estimation and state filtering - Bayesian approach. Kalman filter for white noise. Properties of Kalman filter. Kalman filter for colored/correlated noise.
Study targets
No data.
Course outlines
No data.
Exercises outlines
No data.
Literature
Kailath, T. et al., Linear Estimation, Prentice Hall 1999,
ISBN 0-13-022464-2
Requirements
No data.