Estimation and filtering
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| XP35ESF1 | ZK | 4 | 2P+2C | Czech |
- Course guarantor:
- Vladimír Havlena
- Lecturer:
- Vladimír Havlena
- Tutor:
- Vladimír Havlena
- Supervisor:
- Department of Control Engineering
- Synopsis:
-
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:
-
Kailath, T. et al., Linear Estimation, Prentice Hall 1999,
ISBN 0-13-022464-2
- Note:
- Time-table for winter semester 2024/2025:
- Time-table is not available yet
- Time-table for summer semester 2024/2025:
- Time-table is not available yet
- The course is a part of the following study plans:
-
- Doctoral studies, daily studies (compulsory elective course)
- Doctoral studies, combined studies (compulsory elective course)
- Cybernetics and Robotics (compulsory elective course)