Estimation and Filtering
The course is not on the list Without time-table
| Code | Completion | Credits | Range |
|---|---|---|---|
| XP35OFD | ZK | 4 | 2P+2C |
- Course guarantor:
- Lecturer:
- Tutor:
- Supervisor:
- Department of Control Engineering
- Synopsis:
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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.
- Requirements:
- Syllabus of lectures:
- Syllabus of tutorials:
- Study Objective:
- Study materials:
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Kailath, T. et al., Linear Estimation, Prentice Hall 1999,
ISBN 0-13-022464-2
- Note:
- Further information:
- https://moodle.dce.fel.cvut.cz/course/view.php?id=14
- No time-table has been prepared for this course
- The course is a part of the following study plans:
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- Doctoral studies, daily studies (compulsory elective course)
- Doctoral studies, combined studies (compulsory elective course)
- Doctoral studies, structured daily studies (compulsory elective course)
- Doctoral studies, structured combined studies (compulsory elective course)