EE 527
Detection and Estimation Theory
Spring 2009
T R 2:10-3:30, 204 Marston
- Instructor: Aleksandar Dogandzic
- Room: 3119 Coover
- E-mail:
- Office hours: M W 1-2
- Text: S.M. Kay,
Fundamentals of Statistical Signal Processing:
Estimation Theory. Prentice Hall, 1993, pt. I.
- Reference books:
- S.M. Kay, Fundamentals of Statistical Signal
Processing: Detection Theory. Prentice Hall,
1998, pt. II.
-
B.C. Levy, Principles of Signal Detection and Parameter
Estimation. Springer-Verlag, 2008.
- H.V. Poor,
An Introduction to Signal Detection and Estimation, 2nd ed.
Springer-Verlag, 1994.
- A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin,
Bayesian Data Analysis, 2nd ed. Chapman & Hall, 2004.
-
L. Wasserman, All of Statistics: A Concise Course in Statistical Inference. Springer-Verlag, 2004.
- Course Outline:
- Estimation Theory
- Background material,
- Cramér-Rao bound (CRB),
- Minimum variance unbiased estimation (MVUE),
best linear unbiased estimation (BLUE),
- Maximum likelihood estimation (MLE)
-
expectation-maximization (EM) algorithm,
-
Newton-Raphson and Fisher scoring algorithms,
- Bayesian inference, sequential Bayesian approach and Kalman
filter,
Bayesian EM algorithm,
- An introduction to Monte Carlo (MC) and Markov chain Monte Carlo (MCMC) methods, particle filters,
- Hidden Markov models, forward and backward recursions, Viterbi algorithm,
- An introduction to probabilistic graphical
models,
- Signal-processing, NDE, and communications applications.
- Detection Theory
- Background material,
- Bayes detectors,
- Neyman-Pearson detectors (matched filter, estimator-correlator, ...),
- Multiple hypothesis tests,
- Signal-processing, NDE, and communications applications.
- Grading: (tentative)
- 30% Homework and projects,
- 40% Midterm examinations,
- 30% Final examination.
- Final Exam: 12-2 on May 4.
Interesting papers
Acknowledgment
Useful links