Syllabus (Core): M.Tech

Estimation and Identification

Code: EE 551 | L-T-P-C : 3-0-0

Course Contents:

Estimation and identification – overview and preliminaries, Introduction to linear least squares estimation, Estimator properties – error bounds and convergence, Maximum likelihood estimation, Maximum a posteriori estimation, Linear mean squared estimation, Unmeasured disturbances and Kalman filter, Extended Kalman filter and Unscented Kalman filter for nonlinear systems, Frequency Response Identification – ETFE, ARX and ARMAX models for linear system identification, Recursive approaches for linear systems – RLS, ELS, RML, Introduction to nonlinear system identification – NARX, NRMAX models, Conditions on experimental data, Convergence properties of the identified model

Textbooks / References:

1. S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall, 1993
2. R. L. Eubank, Kalman filter primer, Chapman & Hall, 2006.
3. L. Ljung, System identification: theory for the user,. 2E, Prentice Hall, 1999
4. R. Pintelon and J. Schoukens, System identification: a frequency domain approach, Wiley & Sons, 2012
5. S. A. Billings, Nonlinear system identification: narmax methods in the time frequency and spatio temporal domains, Wiley , 2013