Estimation, filtering and detection

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Code Completion Credits Range Language
B3M35OFD Z,ZK 6 2P+2C Czech
Garant předmětu:
Vladimír Havlena
Vladimír Havlena
Jan Hauser, Vladimír Havlena, Jaroslav Tabaček
Department of Control Engineering

This course will cover description of the uncertainty of hidden variables (parameters and state of a dynamic system) using the probability language and methods for their estimation. Based on bayesian problem formulation principles of rational behavior under uncertainty will be analyzed and used to develop algorithms for parameter estimations (ARX models, Gaussian process regression), filtering (Kalman filter) and detection (likelihood ratio theory) . We will demonstrate numerically robust implementation of the algorithms applicable in real life problems for the areas of industrial process control, robotics and avionics.


Basics of dynamic system theory, probability and statistics.

Syllabus of lectures:

1. Review of basic concepts of statistics

2. MS, LMS and ML estimation

3. Bayesian approach to uncertainty description, model of dynamic system

4. Identification of ARX model parameters

5. Tracking of time varying parameters, forgetting, prior information

6. Numerically robust algorithms for parameter estimation

7. Gaussian process regression

8. Stochastic system, probabilistic state definition, Kalman filter

9. Kalman filter for colored noise, extended Kalman filter

10. Stochastic dynamic programming, LQ and LQG controller, certainty equivalence principle

11. Fault detection and isolation methods

12. Likelihood ratio - theory and applications

13. Nonlinear estimation - local vs. global approximation

14. Monte Carlo methods

Syllabus of tutorials:

Individual assigments - implementation of selected algorithms in Matlab, solution of individual technical problems. Deliverables: running algorithm, technical report.

Homeworks: theoretical assignments. Deliverables: report.

Study Objective:

Ability to solve engineering problems in the area of estimation and filtering, using rigorous theoretical background.

Study materials:

Lewis, F. L., L. Xie, D. Popa: Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, CRC Press, 2005. ISBN 978-1-4200-0829-6

Simon, D.: Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. Wiley, 2006, ISBN: 978-0-471-70858-2

Lectures - published on WEB/Moodle

Assignments-homework - published on WEB/Moodle

Further information:
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:
Data valid to 2024-06-16
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