Inhalt
[ 489WSIVOASU14 ] UE Optimum and Adaptive Signal Processing Systems
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| Es ist eine neuere Version 2023W dieser LV im Curriculum Master's programme Artificial Intelligence 2024W vorhanden. |
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| Workload |
Education level |
Study areas |
Responsible person |
Hours per week |
Coordinating university |
| 1,5 ECTS |
M2 - Master's programme 2. year |
(*)Informationselektronik |
Mario Huemer |
1 hpw |
Johannes Kepler University Linz |
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| Detailed information |
| Original study plan |
Master's programme Information Electronics 2015W |
| Objectives |
Design of optimum estimation algorithms for signal processing problems, Design of optimum and adaptive filters, Design of Kalman filters.
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| 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)
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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 |
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.
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| Changing subject? |
No |
| Further information |
Language can be switched to English if requested
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| On-site course |
| Maximum number of participants |
35 |
| Assignment procedure |
Assignment according to sequence |
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