Catalogue 2019 - 2020

EE 625 Stochastic Processes - Kalman Filters

Credits

3 cr.

Prerequisite

Graduate standing

Course Description

This course covers the basic principles of stochastic processes and control systems. Students learn and review summary state space representations for continued and discrete systems, random variables, and processes. In addition they learn random processes, moments of random processes, and statistical properties of outputs of stochastic systems as well as analysis and design of Kalman filters. Students also learn to use MATLAB computational software to understand new concepts and to perform and implement system analysis and design techniques. The methods of assessing student learning in this course are homework assignments, classroom discussions, design projects, and a final exam.

Fee