Concordia University
Department of Electrical and Computer Engineering
ELEC611: Detection and Estimation Theory
Fall 1999

Instructor: Dr. Yousef R. Shayan

Course Objective:

The objective of this course is to introduce students to the fundamental concepts of detection and estimation theory. At the end of the semester, students should be able to cast a generic detection problem into a hypothesis testing framework and to find the optimal test for the given optimization criterion. They should also be capable of finding optimal estimators for various signal parameters, derive their properties and assess their performance.

Prerequisite:

ENCS 616

Topics to be covered:

• Detection Theory:

• Hypothesis testing: Likelihood Ratio Test, Bayes’ Criterion, Minimax Criterion, Neyman-Pearson Criterion, Sufficient Statistics, Performance Evaluation.
• Multiple hypothesis testing.
• Composite hypothesis testing.
• Sequential detection
• Detection of known signals in white noise.
• Detection of known signals in colored noise.
• Detection of known signals in noise: signal-to-noise criterion.
• Detection of signals with unknown parameters.
• Non-parametric detection.

• Estimation Theory:

• Bayesian parameter estimation.
• Non-Bayesian parameter estimation.
• Properties of estimators: sufficient statistics, bias, consistency, efficiency, Cramer-Rao bounds.
• Linear Mean-Square Estimation.
• Waveform Estimation.

Text:

M. D. Srinath, P. K. Rajasekaran, and R. Viswanathan, “Introduction to Statistical Signal Processing with Applications,” Prentice-Hall, 1996.

References:

H. L. Van Trees, “Detection, Estimation, and Modulation Theory,” John Wiley & Sons, 1968.

J. M. Wozencraft, and I. M. Jacob, “Principles of Communication Engineering,” John Wiley & Sons, 1965.

Grading Scheme:

• Midterm: 40%
• Final: 60%