EE 538 Detection and Estimation Theory 

Catalog Data: Classical statistical decision theory, decision criteria and composite hypothesis tests. Receiver operating characteristics and error probability, applications to radar and communications. Detection of signals with unknown and random parameters, detection of stochastic signals, nonparametric detection techniques. Introduction to signal design, ambiguity function, the uncertainty principle. Applications to radar and sonar systems.

Goals: The goal of this course is to introduce the students to the basic theory of signal detection and estimation.

Textbook: An Introduction to Signal Detection and Estimation (Second Edition), H. Vincent Poor, Springer-Verlag, 1994.

References:

1. Detection Estimation and Modulation Theory, 2nd Edition, Part I, Detection, Estimation, and Filtering Theory, Harry L. Van Trees, Kristine L. Bell with Zhi Tian, John Wiley & Sons, Inc., 2013.
2. Fundamentals of Statistical Signal Processing, Vol 2: Detection Theory, Steven M. Kay, Prentice Hall, 1998.
3. Fundamentals of Statistical Signal Processing, Vol 1: Estimation Theory, Steven M. Kay, Prentice Hall, 1993.
4. Principles of Signal Detection and Parameter Estimation, Bernard C. Levy, Springer Science+Business, LLC, 2008
5. Statistical Signal Processing; Detection, Estimation and time Series Analysis, Louis L. Scharf, Addison-Wesley, 1990.
6. Lessons in Estimation Theory for Signal Processing, Communications, and Control, Jerry M. Mendel, Prentice Hall, PTR, 1995.

Instructor: Dr. Mustafa A. Altinkaya, Professor

Class Hours : Wednesday 9:45 a.m. - 12:30 p.m.

Prerequisites: Basics of applied probability, including random vectors and conditional expectations,

Topics:

1. Hypothesis Testing
   • Bayesian Hypothesis Testing
   • Minimax Hypothesis Testing
   • Neyman-Pearson Hypothesis Testing
   • Composite Hypothesis Testing
2. Signal Detection in Discrete Time
   • Models and Detector Structures
   • Performance Evaluation of Signal Detection Procedures
   • Nonparametric and Robust Detection
3. Elements of Parameter Estimation
   • Bayesian Parameter Estimation
   • Nonrandom Parameter estimation
   • Maximum Likelihood Estimation
4. Elements of Signal Estimation
   • Kalman-Bucy Filtering
   • Linear Estimation
   • Wiener-Kolmogorov Filtering
5. Further Topics (tentative)
   • Sequential Estimation
   • Expectation Maximization Algorithm

Exercises : End of chapter exercises. Selected problems will be considered and discussed after 2 weeks from assignment (will not be collected).

Grading : midterm exam  30%, term project 30%, final exam 40%
(Exams will be in take home format with no longer time as for an in-class exam)