ECE6555 Course Syllabus

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