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| Detailed information |
| Original study plan |
Master's programme Electronics and Information Technology 2017W |
| 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 |
Oral exam
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| Methods |
Theory presented by lecturer, Matlab based presentations
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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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| Corresponding lecture |
(*)489WSIVOASV14: VL Optimale und Adaptive Signalverarbeitungssysteme (3 ECTS)
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| Earlier variants |
They also cover the requirements of the curriculum (from - to)
489WSIVOASV14: VL Optimum and Adaptive Signal Processing Systems (2014W-2017S)
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