Search
Search form
ECE Course Outline
ECE6555
Optimal Estimation (3-0-3)
- Prerequisites
- ECE 6550
- Corequisites
- None
- Catalog Description
- Techniques for signal and state estimation in the presence of measurement and process noise with the emphasis on Wiener and Kalman filtering.
- Textbook(s)
- Kamen & J.K. Su, Introduction to Optimal Estimation, Springer Verlag, 1999. ISBN 9781852331337 (required)
- Topical Outline
Introduction (1 week) Signal estimation State estimation Least squares estimation Stochastic theory (2 weeks) Review of probability theory Random variables and signals Systems with random inputs Optimal Estimation (1 and 1/2 weeks) Maximum likelihood estimation Maximum a posteriori estimation Minimum mean square error estimation Wiener Filtering (2 and 1/2 weeks) FIR Wiener filter Noncausal IIR Wiener filter Causal Wiener filter Kalman filtering (5 weeks) Derivation of the Kalman filter Filter properties Steady-state Kalman filter Prediction and smoothing Applications Nonlinear Filtering (2 weeks) The extended Kalman filter The Levenberg-Marquardt measurement update Applications
Georgia Institute of Technology
North Avenue, Atlanta, GA 30332
Phone: 404-894-2000