227-0101-00L  Discrete-Time and Statistical Signal Processing

SemesterAutumn Semester 2020
LecturersH.‑A. Loeliger
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
227-0101-00 GDiscrete-Time and Statistical Signal Processing
The lecturers will communicate the exact lesson times of ONLINE courses.
4 hrs
Tue14:00-18:00ON LI NE »
H.‑A. Loeliger

Catalogue data

AbstractThe course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.
ObjectiveThe course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter.
Content1. Discrete-time linear systems and filters:
state-space realizations, z-transform and spectrum,
decimation and interpolation, digital filter design,
stable realizations and robust inversion.

2. The discrete Fourier transform and its use for digital filtering.

3. The statistical perspective:
probability, random variables, discrete-time stochastic processes;
detection and estimation: MAP, ML, Bayesian MMSE, LMMSE;
Wiener filter, LMS adaptive filter, Viterbi algorithm.
Lecture notesLecture Notes

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersH.-A. Loeliger
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 180 minutes
Written aidsLecture Notes (not including problems and solutions) and personal notes (max.4 pages). No electronic devices. (Pocket calculators will be handed out, if necessary.)
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkInformation
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Biomedical Engineering MasterRecommended Elective CoursesWInformation
Data Science MasterCore ElectivesWInformation
Electrical Engineering and Information Technology Bachelor5th Semester: Third Year Core CoursesWInformation
Electrical Engineering and Information Technology MasterFoundation Core CoursesWInformation
Electrical Engineering and Information Technology MasterFoundation Core CoursesWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Electrical Engineering and Information Technology MasterSpecialisation CoursesWInformation
Electrical Engineering and Information Technology MasterSpecialisation CoursesWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Mathematics MasterInformation and Communication TechnologyWInformation
Quantum Engineering MasterElectivesWInformation