 |
| Detailed information |
| Original study plan |
Master's programme Electronics and Information Technology (ELIT) 2023W |
| Objectives |
Students know and understand the fundamental parameter estimation methods, the basics of optimum filters, adaptive filters and Kalman filters qualitatively and mathematically and can apply them to in-depth problems and generalize the results obtained.
|
| Subject |
- Parameter Estimation
- Classical Methods: MVU, BLUE, ML, LS
- Bayesian Methods: MAP, MMSE, LMMSE
- Applications: amplitude estimation, frequency estimation, power estimation, signal extraction, system identification, data estimation
- Optimum Filters
- Wiener Filters
- Least Squares Filters
- Applications: system identification (channel estimation), inverse system identification (e.g. for channel equalization), noise reduction, linear prediction (e.g. for voice signals)
- Adaptive Filters
- LMS (Least Mean Squares) algorithm
- RLS (Recursive Least Squares) algorithm
- Kalman Filters
- Standard Kalman Filter
- Extended Kalman Filter
- Applications
|
| Criteria for evaluation |
Written or oral (depending on the number of subscribed students)
|
| Methods |
Lecture using slides and blackboard, Matlab based presentations
|
| Language |
English and French |
| 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 |
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
489INTEOASV17: VL Optimum and Adaptive Signal Processing Systems (2017W-2022S)
489WSIVOASV14: VL Optimum and Adaptive Signal Processing Systems (2014W-2017S)
|
|
