course: Statistical Signal Processing

number:
141222
teaching methods:
lecture with tutorials
media:
overhead transparencies, Moodle, 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

All statements pertaining to examination modalities (for the summer/winter term of 2020) are given with reservations. Changes due to new requirements from the university will be announced as soon as possible.

Date according to prior agreement with lecturer.

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

goals

Students know important stochastic processes, that are used to model measured signals. They can select suitable models for the most common applications, understand their properties, and can apply these models e.g. for parameter estimation. Students have acquired subject-specific knowledge of important standard methods of stochastic signal processing (e.g. Kalman filters, adaptive filters, Markov chains and Markov chain Monte Carlo methods) and are able to apply them to known and new problems. Through the exercises and computer exercises (practical exercise), students are enabled to apply the acquired knowledge in a team, to explain and evaluate solution approaches and to discuss them. The important basic concepts of stochastic signals are also taught in English, so that students are able to access the international literature in the field of statistical signal processing.

Translated with www.DeepL.com/Translator (free version)

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.

literature

  1. Kay, Steven M. "Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory", Prentice Hall, 1993
  2. Kay, Steven M. "Fundamentals of Statistical Signal Processing, Volume II: Detection Theory ", Prentice Hall, 1998
  3. Kay, Steven M. "Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development ", Prentice Hall, 2013
  4. Kay, Steven M. "Intuitive Probability and Random Processes using MATLAB", Prentice Hall, 2005

miscellaneous

Die Vorlesungs- und Übungsunterlagen werden über Moodle zur Verfügung gestellt. Die notwendigen Informationen erhalten Sie in der ersten Vorlesung.