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ECE 7030: Detection and Estimation Theory

3 Credits
Foundations of detection theory, including Neyman-Pearson, Bayes, and Minimax Bayes detection. Maximum likelihood and Bayes estimation theory. Recursive estimation and Kalman filtering and smoothing. Expectation maximization and hidden Markov models.

Prerequisite/Restriction: ECE 6010, ECE 6030

Semester(s) Traditionally Offered: FALL

Course Syllabus

Course Instructor: Mohammad Shekaramiz

Textbook: Mathematical Methods and Algorithms for Signal Processing

Topics Covered: 1. Introduction and Overview, Probability review and notation; 2. Estimation theory; 3. ML principle and ML estimation; 4. Biased and unbiased estimators; 5. Minimum variance unbiased estimator (MVUE), Best linear unbiased estimator (BLUE), Cramer-Rao (CR) Bounds; 6. EM algorithm, Hidden Markov models and related models; 7. Bayes estimation; 8. Kalman filtering, particle filtering, unscented Kalman filters; 9. Gaussian process models; 10. Detection theory; 11. Neyman-Pearson detection; 12. Bayes detection, MAP; 13. Some applications