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 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.
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
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.
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.
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 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.
Prerequisite
Basic knowledge of probability theory and stochastic processes.
Mer information
Tomas McKelvey
Telephone: 031-772 8061
E-mail: tomas.mckelvey@chalmers.se
Telephone: 031-772 8061
E-mail: tomas.mckelvey@chalmers.se
Föreläsare
Tomas McKelvey
E-mail: tomas.mckelvey@chalmers.se
