course: Statistical Signal Processing
- teaching methods:
- lecture with tutorials
- computer based presentation, black board and chalk
- responsible person:
- Prof. Dr.-Ing. Georg Schmitz
- Prof. Dr.-Ing. Georg Schmitz (ETIT), wiss. Mitarbeiter (ETIT)
- offered in:
- winter term
dates in winter term
- start: Wednesday the 16.10.2019
- lecture Wednesdays: from 10:15 to 11.45 o'clock in ID 03/419
- tutorial Tuesdays: from 08:15 to 09.45 o'clock in ID 03/419
Date according to prior agreement with lecturer.
|Form of exam:||oral|
|Registration for exam:||FlexNow|
The students have acquired the ability to apply important standard methods of stochastic signal processing to different problems. For this purpose, the specific basic knowledge was acquired. Through computer tutorials in small groups, the students are able to put what they have learned into practice in a team.
The lecture 'Statistical Signal Processing' introduces stochastic signal models, and some important engineering applications of stochastic signals. First, the most important stochastic processes for signal models, such as white noise, Poisson processes or Markov chains, are discussed. For the applications, the lecture focuses on discrete-time optimal filtering techniques. Here, the focus is on the Kalman filter, which is derived for the example of one-step forward prediction. Subsequently, selected methods of stochastic signal processing are discussed, including in particular parametric and nonparametric spectral estimation, maximum-likelihood estimators, detectors, and adaptive filters (LMS, RLS).
Knowledge of stochastic signals corresponding to those taught in the lecture "Stochastic Signals" in the Bachelor's programme Electrical Engineering and Information Technology.