Departments' graduate courses

Course start and periodicity may vary. Please see details for each course for up-to-date information. The courses are managed and administered by the respective departments. For more information about the courses, how to sign up, and other practical issues, please contact the examiner or course contact to be found in the course information. 

Model-based signal processing

  • Course code: FSSY065
  • Course higher education credits: 7.5
  • Graduate school: Signals and Systems
  • Course start: 2022-08-29
  • Course end: 2022-10-30
  • Course is normally given: Every second year,next time in SP1 in 2022
  • Language: The course will be given in English

Model-based signal processing is given in master's programme Communication Engineering. Course information is available in Studieportalen. See Model-based signal processing

This course offers an introduction to detection and estimation theory
with emphasis on applications in signal processing and commmunications.
It provides an overview of the main tools to extract desided information
from measured signals under a statistical signal processing framework.
Fundamental limitations on the estimation performance is discussed. A
brief introduction to detection theory and classification is presented.

Minimum Variance Unbiased Estimation and the Cramer-Rao Lower Bound,
Best Linear Unbiased Estimators, Maximum Likelihood Estimation, Least
Squares, Method of Moments, Bayesian Estimation, Wiener and Kalman
Filters, Introduction to Detection Theory, Likelihood Ratio Tests, Model
Order Selection.

Learning Outcomes

  • 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
  • Know how to choose a suitable estimation approach for a given data model and derive the resulting equations
  • Know how to implement a variety of estimators on computer
  • Know how to evaluate the performance of estimators and compare different approaches
  • Have a brief knowledge about detection techniques and selection of a suitable model order

Main Text
Steven M. Kay, “Fundamentals of Statistical Signal Processing,
Estimation Theory”. Prentice-Hall, Englewood Cliffs, N.J., 1993.

Basic knowledge of probability theory and stochastic processes.

Tomas McKelvey E-mail:
More information
Tomas McKelvey
Telephone: 031-772 8061

Page manager Published: Wed 10 Feb 2021.