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Syllabus for

Academic year
ESS150 - Statistical digital signal processing
 
Owner: EMAST
3,5 Credits (ECTS 5,25)
Grading: TH - Five, Four, Three, Not passed
Level: A
Department: 0739 - Signaler och system E


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0101 Project 3,5 c Grading: TH   3,5 c    

In programs

TTFYA ENGINEERING PHYSICS, Year 4 (elective)
EMAST MSc PROGR. IN DIGITAL COMMUNICATION SYSTEMS AND TECHNOLOGY, Year 1 (compulsory)
TELTA ELECTRICAL ENGINEERING, Year 4 (elective)
TAUTA AUTOMATION AND MECHATRONICS ENGENEERING, Year 4 (elective)
TKEFA CHEMICAL ENGINEERING WITH ENGINEERING PHYSICS, Year 4 (elective)
TDATA COMPUTER SCIENCE AND ENGINEERING - Communications System, Year 4 (elective)
TDATA COMPUTER SCIENCE AND ENGINEERING, Year 3 (elective)

Examiner:




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

Deterministic and stochastic signal modeling using linear and non-linear least-squares. Application in speech coding and classification. Optimal filtering using the Wiener and Kalman filter. Application in interference suppression and tracking. Spectrum estimation using Fourier-based and parametric (high-resolution) techniques. Application in ECG analysis and spatial spectrum estimation (direction finding). Adaptive filtering using the LMS and RLS algorithms. Application in adaptive interference suppression, channel equalization and adaptive antennas.

Organisation

The course is primarily given in the form of lectures and supervised problem-solving sessions (some are computer sessions). There are also some hand-in problems and unsupervised computer projects in Matlab. A real-time laboratory project is also included to give hands-on experience of the methods.

Literature

M. H. Hayes: Statistical Digital Signal Processing and Modeling, John Wiley & Sons, New York 1996.

Examination

A written examination is given at the end of the course. A computer project and the real-time lab are mandatory.


Page manager Published: Thu 03 Nov 2022.