Om kursen
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 estimation and detection theory
with emphasis on applications in signal processing and commmunications. It
provides an overview of the main tools to extract desired 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.
Content
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 estimator
- Know how to implement a variety of
estimators on a 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.
Prerequisite
Basic
knowledge of probability theory and stochastic processes.
Mer information
Telephone: 031-772 8061
E-mail: tomas.mckelvey@chalmers.se
