Instructor: Martin Hagan
Office: 311 E.S. Phone: (405) 744-7340
Email: mhagan (at) okstate.edu
Lessons
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Estimation Theory for Signal Processing, Communications, & Control
Author: - Mendel
Publisher: Prentice Hall, 1995
There are two main topics covered in this course: parameter estimation and state estimation for discrete-time systems. The course will cover a number of methods for parameter estimation, including least squares, maximum likelihood and Bayesian techniques. The course will also address applications of the developed models to problems in prediction, control, signal processing, and coding.
State estimation is the process of estimating dynamic hidden variables based on a set of related measurable variables. The principal technique for state estimation in linear systems is the Kalman filter (a generalization of the Weiner filter). In the second half of this course we will develop the Kalman filter in a simple step-by-step process. We will also cover state estimation for nonlinear systems, using the extended Kalman filter, the unscented Kalman filter and the particle filter. We will also discuss applications of state estimation to problems in prediction, control, inertial navigation, etc.
Prerequisite: ECEN 5513 or equivalent background in random systems
The course grade will be based on two examinations, homeworks and quizzes, and a term project. For the project you will test several state estimation methods on a practical nonlinear control system.
ECEN/MAE 5513, or equivalent background in probability.