| Dozent: | Prof. Dr.-Ing. Walter Kellermann | |
| Übungsleiter: | M.Sc. Roland Maas | |
| Termin | Vorlesung: | Mo 16:00 - 17:30, N 5.17 Fr 12:15 - 13:45, H6 |
| Übung: | Mi 16:15- 17:45, H6 | |
| Leistungspunkte: | 5 ECTS | |
| Voraussetzungen: | Signale und Systeme I, Signale und Systeme II, Wahrscheinlichkeitsrechnung oder Stochastische Prozesse | |
| Sprache der Veranstaltung: | Englisch | |
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Content
The course concentrates on fundamental methods of statistical signal processing and their applications. The main topics are:
- Discrete-time stochastic processes in the time and frequency domain
- Estimation theory
- Non-parametric and parametric signal models (pole/zero models, ARMA models)
- Optimum linear filters (e.g. for prediction), eigenfilters, Kalman filters
- Algorithms for optimum linear filter identification (adaptive filters)
Course material
The lecture slides and the exercise handouts are available on StudOn.
Extra points for the written exam
Extra points for the written exam can be obtained by handing in the homework. The homework is to be prepared in groups of two. Copying from another group will result in zero points.
Important notice: If you fail in the exam without extra points, they cannot be taken into account. The extra points expire for the resit as well.
Number of passed worksheets: |
8 |
7 |
6 |
5 |
4 |
<4 |
Extra points for the written exam: (based on 100 achievable points) |
10 |
8 |
6 |
4 |
2 |
0 |
Literature
- A. Papoulis, S. Pillai: Probability, Random Variables and Stochastic Processes; McGraw-Hill, 2002 (english)
- D. Manolakis, V. Ingle, S. Kogon: Statistical and Adaptive Signal Processing; Artech House, 2005 (english)
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