Estimation and Filtering
Code | Completion | Credits | Range |
---|---|---|---|
XD35OFI | Z,ZK | 4 | 19+2s |
- Lecturer:
- Tutor:
- Supervisor:
- Department of Control Engineering
- Synopsis:
-
The objective of the subject is to introduce parametr estimation and state filtering methods from a unified bayesian viewpoint. Methods for the estimation of parameters of ARX models and filtering of state of a dynamic system including implementation and numerically robust algorithms, as well as Monte Carlo methods, are studied in details. Also, basic fault detection and isolation methods based on multiple models are introduced.
- Requirements:
-
Submission of seminary work results including a hardcopy of the report , presentation.
- Syllabus of lectures:
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1. Introduction
2. Estimation and filtering - bayesian problem formulation
3. One-shot and recursive parameter estimation for constant parameters
4. Tracking of time-varying parameters, forgetting
5. Robust numerical implementation of identification algorithms
6. Utilization of prior information, parallel and alternative models
7. Stochastic system, bayesian definition of state
8. Kalman filter, basic properties
9. Kalman filter for coloured noise
10. Simultaneous state estimation and arameter tracking
11. Extended Kalman filter, applications
12. Smoothing, backward Kalman filter
13. Nonlinear estimation and filtering
14. Monte Carlo implementation, sampling/resampling algorithms
- Syllabus of tutorials:
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1. Probability, risk function, uncertainty, game theory and relationships with estimation
2. Methods of estimation, the method of moments, functional methods, likelihood based
3. Maximum likelihood method, recursive likelihood update calculations
4. Bayes paradigm, examples and comarison with the classical solutions
5. Algorithms for recursive parameter estimation and their implementation
6. Seminary work setting, QR and LDU decomposition and their application for estimation
7. Hybrid and parallel models
8. Presentation of proposed solutions to the seminary works
9. Algorithms for system state estimation
10. Kalman filter
11. Non-linear estimation via linearization, non-linear least squares via Gauss-Newton method, Extended Kalman filter of the 1st and 2nd order
12. Smoothing
13. Numerical methods, bootstrap, sampling-resamplig
14. Presentation of seminary works
- Study Objective:
- Study materials:
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1. Lewis, F.L.: Optimal Estimation. J.Wiley and Sons, N.Y. 1986, 1993
2. Ljung, L.: System identification/Theory for the user. Springer V., N.Y. 1989
3. Box, Jenkins: Time series analysis, Prentice Hall, 1994
- Note:
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans:
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- Cybernetics and Measurements - Control Engineering- structured studies (compulsory elective course)
- Cybernetics and Measurements - Artificial Intelligence- structured studies (compulsory elective course)
- Cybernetics and Measurements - Measurement and Instrumentation Systems- structured studies (compulsory elective course)
- Cybernetics and Measurements - Aeronautical Engineering and Control Systems- structured studies (compulsory elective course)