Inhalt
[ 489MAITOASU22 ] UE Optimum and Adaptive Signal Processing Systems
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| Workload |
Education level |
Study areas |
Responsible person |
Hours per week |
Coordinating university |
| 1,5 ECTS |
M1 - Master's programme 1. year |
(*)Informationselektronik |
Mario Huemer |
1 hpw |
Johannes Kepler University Linz |
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| 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.
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| 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
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| Criteria for evaluation |
Home exercises have to be handed in during the semester, short oral final interview
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| Methods |
Examples presented by lecturer, home exercises
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| Language |
English |
| 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.
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| Changing subject? |
No |
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
489INTEOASU17: UE Optimum and Adaptive Signal Processing Systems (2017W-2022S)
489WSIVOASU14: UE Optimum and Adaptive Signal Processing Systems (2014W-2017S)
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| On-site course |
| Maximum number of participants |
35 |
| Assignment procedure |
Assignment according to sequence |
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