course: Statistical Signal Processing

number:
141222
teaching methods:
lecture with tutorials
media:
computer based presentation, black board and chalk
responsible person:
Prof. Dr.-Ing. Georg Schmitz
Lecturers:
Prof. Dr.-Ing. Georg Schmitz (ETIT), wiss. Mitarbeiter (ETIT)
language:
german
HWS:
4
CP:
5
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

Exam

Date according to prior agreement with lecturer.

Form of exam:oral
Registration for exam:FlexNow
Duration:30min

goals

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.

content

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).

requirements

keine

recommended knowledge

Knowledge of stochastic signals corresponding to those taught in the lecture "Stochastic Signals" in the Bachelor's programme Electrical Engineering and Information Technology.