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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
SSY215 - Model-Based Signal Processing, advanced level
Syllabus adopted 2008-02-23 by Head of Programme (or corresponding)
Owner: MPCOM
7,5 Credits
Grading: UG - Fail, pass
Education cycle: Second-cycle
Major subject: Electrical Engineering

Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0108 Project 7,5c Grading: UG   7,5c    

In programs

MPCOM COMMUNICATION ENGINEERING, MSC PROGR - Research Specialization, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR - Special research and PhD course, Year 1 (elective)


Professor  Mats Viberg


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

Knowledge of signals and systems (Fourier analysis, filters etc.) in continuous and discrete time. Basic knowledge of probability theory and stochastic processes.


Signal processing is really about manipulating measured signals to extract some desired information. Early on, the processing was mainly linear filters and transforms; but with todays technology, much more complicated signal processing algorithms are running in real-time. Several properties of the signal and noise are often unknown, and are learned directly from data (adaptive signal processing). The information content is often given in the form of a physical data model, and the uncertainty is described in terms of statistics. For example, a radar return from a point-like target is easily modeled as a function of its distance and direction, whereas the reflections from unwanted objects (clutter) are considered random. These models are then exploited to extract the sought information. The procedure is often termed parameter estimation. The purpose of this course is to give an overview of the most important approaches to solve such model-based estimation problems. Fundamental limitations on the estimation process are also discussed. The statistical estimation techniques are frequently illustrated using examples from various signal processing applications. A brief introduction to detection theory and classification is also given.

Learning outcomes (after completion of the course the student should be able to)

Understand how to model experimental data in simple situations, and make suitable assumptions on the random components in the data
Understand the fundamental limitations of statistical data models, and how these can be used to design experiments
Understand the trade-offs involved in parameter estimation, where computational simplicity is often traded for statistical efficiency
Know how to choose a suitable estimation approch for a given data model and derive the resulting equations/relations
Know how to implement a variety of estimators in Matlab
Know how to evaluate the performance of estimators by computer simulation, and thus compare different approaches
Have a brief knowledge about detection problems and techniques for selecting a suitable model order


Minimum Variance Unbiased Estimation and the Cramer-Rao Lower Bound, Best Linear Unbiased Estimators, Maximum Likelihood Estimation, Least Squares, Method of Moments and Instrumental Variables, Bayesian Estimation, Wiener and Kalman Filters. Introduction to Detection Theory: Binary Hypothesis, Likelihood Ratio Tests, Information-Theoretic Criteria. Application examples from Wireless Communications, Radar Systems and Environmental Measurements.


The course is given in the form of one lecture and one problem-solving session per week. After each lecture, a number of problems are assigned, both theoretical and computer simulations (Matlab). During the problem-solving sessions, the students present their solutions to this week's problems.


Steven M. Kay: "Fundamentals of Statistical Signal Processing: Estimation Theory", Prentice-Hall, Englewood Cliffs, N.J., 1993. Hand-out material.


An examination project in the form of a take-home exam is given at the end of the course. To pass the course requires at least 50% of the points from the project. To access the project further requires solving at least 70% of the problems during the course.

Page manager Published: Thu 04 Feb 2021.