EC EN 672

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Detection and Estimation Theory

Electrical and Computer Engineering Ira A. Fulton College of Engineering

Course Description

Sufficiency, completeness; Neyman-Pearson and Bayes detector; maximum likelihood, Bayes, minimum mean square, and linear estimation; Kalman filters; selected topics.

When Taught

Winter

Min

3

Fixed/Max

3

Fixed

3

Fixed

0

Other Prerequisites

EC En 370 or equivalent; EC En 670; graduate standing or instructor's consent

Title

2.

Learning Outcome

Apply these concepts to selected problems.

Title

1.

Learning Outcome

Understand fundamental concepts in modern Detection and Estimation theory including sufficiency, completeness; Neyman-Pearson and Bayes detector; maximum likelihood, Bayes, minimum mean square, and linear estimation; Kalman filters.