Syllabus for |
|
| ESS150 - Statistical digital signal processing |
| |
| Owner: EMAST |
|
|
3,5 Credits (ECTS 5,25) |
| Grading: TH - Five, Four, Three, Not passed |
| Level: D |
|
Department: 32 - ELECTRICAL ENGINEERING
|
Teaching language: English
| Course module |
|
Credit distribution |
|
Examination dates |
|
Sp1 |
Sp2 |
Sp3 |
Sp4 |
|
No Sp |
| 0101 |
Examination |
3,5 c |
Grading: TH |
|
|
3,5 c
|
|
|
|
|
|
12 Dec 2005 am M, |
21 Apr 2006 am V, |
01 Sep 2006 am V |
In programs
TKEFA CHEMICAL ENGINEERING WITH ENGINEERING PHYSICS, Year 4 (elective)
TDATA COMPUTER SCIENCE AND ENGINEERING, Year 3 (elective)
TDATA COMPUTER SCIENCE AND ENGINEERING - Communications System, Year 4 (elective)
TAUTA AUTOMATION AND MECHATRONICS ENGENEERING, Year 4 (elective)
TTFYA ENGINEERING PHYSICS, Year 4 (elective)
TITEA SOFTWARE ENGINEERING, Year 4 (elective)
TITEA SOFTWARE ENGINEERING, Year 3 (elective)
EMAST MSc PROGR. IN DIGITAL COMMUNICATION SYSTEMS AND TECHNOLOGY, Year 1 (compulsory)
TELTA ELECTRICAL ENGINEERING, Year 4 (elective)
Examiner:
Professor
Irene Gu
Eligibility:
For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.
Course specific prerequisites
ESS010 Signaler och system (Signals and systems) or a corresponding course. ESS170 Tillämpad signalbehandling or ESS145 Applied Signal Processing. TMA421 Stokastiska processer or TMS115 Probability and Stochastic Processes is highly recommended.
Aim
The purpose of this course is to give a deep understanding of statistical digital signal processing. You will learn how underlying models lead to specific algorithms, what performance that can be expected and how the various methods are used in applications. The goal is to be able not only to use existing methods "as is", but also to extend these to suit slightly modified problem formulations.
Content
The course covers theoretical and practical aspects of stochastic signal modeling, optimal and adaptive filtering, and spectral estimation. Signal modeling discussed in this course includes AR, MA, ARMA and harmonic models. For optimal and adaptive filtering, Wiener and Kalman filters, LMS algorithm will be studied. For spectral estimation, both non-parametric and parametric methods will be studied. To gain practical experience, various applications will be discussed in combination with group project work.
Organisation
The course mainly consists of three parts: lectures that explain the basic theories on: (1) signal modeling, (2) optimal/adaptive filtering, (3) spectral estimation; tutorials that supervise problem-solving sessions; and group projects where each group will seek 3 practical applications, corresponding to applications of the theories in (1) (2) and (3).
Literature
M. H. Hayes: Statistical Digital Signal Processing and Modeling, John Wiley & Sons, New York 1996.
Examination
Combination of a written examination (on basic theories), home work, and the results of group projects.