Estimation Theory

Learning Outcomes

  1. Explain basic concepts in state estimation and system identification and their relation to corresponding mathematical background
  2. Develop the technique of linear estimation in static systems for corresponding problems
  3. Use Kalman filter and Extended Kalman filter in applications
  4. Use non-parametric identification methods for linear systems in applications
  5. Use parametric identification methods for linear systems in applications
  6. Use corresponding model structure in identification and method for validation of the identified model
  7. Generate practical implementations of estimation methods

Forms of Teaching

Lectures

Laboratory

Week by Week Schedule

  1. Basic concepts in estimation, Review of background techniques (linear algebra, probability theory and stochastic processes, statistics)
  2. Linear estimation in static systems
  3. Additional topics in Kalman filtering
  4. Additional topics in Kalman filtering
  5. Additional topics in Kalman filtering
  6. Extended Kalman filter
  7. Extended Kalman filter
  8. Midterm exam
  9. Basic terms and mathematical concepts used in system identification, Classification of system identification methods, Correlation analysis for continuous-time systems, Spectral analysis for continuous-time systems
  10. Correlation analysis for discrete-time systems, Spectral analysis for discrete-time systems, Parametric models: deterministic and stochastic parts, Least-squares method, direct and recursive
  11. Parametric models: deterministic and stochastic parts, Least-squares method, direct and recursive, Instrumental variable method, direct and recursive, Maximum-likelihood method, Identification methods with forgetting factor
  12. Instrumental variable method, direct and recursive, Maximum-likelihood method, Identification methods with forgetting factor
  13. Identification methods for MIMO systems, State-space identification methods
  14. Estimation of model order and delay for identiication, Procedures for validation of the identified models, Practical procedure and recommendations for identification
  15. Final exam

Study Programmes

University graduate
Control Systems and Robotics (profile)
(2. semester)

Literature

(.), L. Ljung. System Identification: Theory for the User. Prentice Hall, New Jersey, 1999.,
(.), Simon Haykin. Kalmand Filtering and Neural Networks,

For students

General

ID 223688
  Summer semester
5 ECTS
L3 English Level
L1 e-Learning
45 Lectures
10 Exercises
12 Laboratory exercises