 |
| Detailed information |
| Original study plan |
Master's programme Mechatronics 2022W |
| Objectives |
Design of optimum estimation algorithms for signal processing problems, Design of optimum and adaptive filters, Design of Kalman filters.
|
| Subject |
- Design of optimum estimation algorithms (CRLB, MVU, BLUE, LS, MMSE, LMMSE, MAP; Applications: amplitude estimation, frequency estimation, power estimation, signal extraction, system identification)
- Optimum filters (Wiener filter; Least squares filter; Applications: system identification, inverse system identification, noise cancellation, linear prediction)
- Adaptive filters (LMS algorithm; RLS algorithm)
- Kalman filter (Kalman filter for linear systems; Extended Kalman filter for non-linear systems)
|
| Criteria for evaluation |
Oral exam
|
| Methods |
Theory presented by lecturer, Matlab based presentation
|
| Language |
Upon agreement with participants – English or German |
| Study material |
- Lecture Slides
- S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall, Rhode Island 1993.
- D.G. Manolakis, V.K. Ingle, S.M. Kogon, Statistical and Adaptive Signal Processing, Artech House, 2005.
|
| Changing subject? |
No |
| Corresponding lecture |
(*)MEMWDVOSTSV: VO Statistische Signalverarbeitung (3 ECTS)
|
|
