Optimal Estimation
(3-0-0-3)
CMPE Degree: This course is Not Applicable for the CMPE degree.
EE Degree: This course is Not Applicable for the EE degree.
Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.
Technical Interest Group(s) / Course Type(s): Systems and Controls
Course Coordinator:
Prerequisites: ECE 6550
Catalog Description
Techniques for signal and state estimation in the presence of measurementand process noise with the emphasis on Wiener and Kalman filtering.
Textbook(s)
Lectures on Wiener and Kalman FilteringCourse Outcomes
Not Applicable
Strategic Performance Indicators (SPIs)
Not Applicable
Topical Outline
1. Introductory Material
a. Vector Spaces and Deterministic State Space Theory
b. Review: Probability, Stochastic Processes and Hilbert Spaces
c. Inverse Problem - Observer Theory
d. Bayesian Estimation
2. Least Squares Estimates and Linear Least Squares Estimates
a. Smoothing for Stationary Processes
b. Spectral Factorization and Wiener Filters
c. Discrete Time Recursive Estimation: Kalman Filter
d. Continuous Time Kalman Filters
e. Stationary Kalman Filters: Relations to Wiener Filters
f. The Riccati Equation
g. Information Forms
3. Complementary Topics:
a. Filtering with Time-correlated Measurement Noise: Johanson Filter
b. Crypto-Deterministic Filtering, Friedland Filter, Kalman Filter Divergence
c. Fast Algorithms and Square Root Forms
d. Scattering Theory and Relation to Filtering
e. Linearized and Extended Kalman Filter
4. Selected Advanced Topics