This is supplemental course information, designed to give you a fuller picture of the course and an expanded look at the topics covered. This is an unofficial document. The USC Course Catalog is the binding description of all university courses. Information such as books, materials covered, and the order of topics is subject to change. Please consult instructor for this semseter to get more upto date course information.
Parameter estimation and state estimation techniques including: least squares, BLUE, maximum-likelihood, maximum a posteriori, Kalman-prediction, Kalman-filtering and Kalman smoothing and extended Kalman filtering. Prerequisite: EE 562a.
“Lessons in Estimation Theory for Signal Processing, Communications, and Control,” Jerry M. Mendel, Prentice-Hall 1997.
Jerry M. Mendel, Professor of Electrical Engineering
Parameter Estimation Methods for Linear Models
Weighted least squares (batch and recursive processing); best linear unbiased estimation (BLUE); maximum likelihood; mean-squared; and, maximum a priori.
Higher-order statistics.
State Estimation
Mean-squared prediction; mean-squared filtering (Kalman filter/Kalman Bucy filter); and, mean-squared smoothing
Estimation for Non-Linear Models
Iterated least squares; Extended Kalman filter; and, maximum-likelihood
Robust Estimation
Regularized weighted least squares and Kalman filtering
To cover famous and frequently used parameter and state estimation techniques and algorithms widely used in many fields, including their derivations and performance analyses (properties) as well as their applications, and to explain how and when many of the algorithms are interrelated.
At the completion of EE 563, the students will be able to:
1) Use all of the major parameter and state estimation algorithms that are currently in use today.
2) Understand and explain how the major estimation algorithms are related, or if they are related.
3) Understand how to establish the performance properties of the major estimation algorithms
There is one for this course, and it changes from year-to-year.
Prepared by: Jerry M. Mendel on 24 November 2003.
