"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
-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
-Have a brief knowledge about detection techniques and selection of a
suitable model order
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.