Academics / Courses / DescriptionsELEC_ENG 426: Signal Detection and Estimation
This course is not currently offered.
Prerequisites
Fourier transform, signals and systems, probabilities and random processes.Description
CATALOG DESCRIPTION: Simple-hypothesis detection problems, Continuous-time signal detection in white noise, Karhunen-Loeve expansion of random processes, detection of signals in colored noise, Detection of signals with random parameters, Bayes and maximum likelihood signal parameter estimation, Nonparametric detection, sequential detection procedures, Distributed detection techniques.
REQUIRED TEXTBOOK: None
REFERENCE TEXTS:
- A. D. Whalen, Detection of Signals in Noises, Academic Press, 2nd Ed. 1995.
- H. L. Vantrees, Detection, Estimation and Modulation Theory, Part 1, John Wiley, 1968.
- C. W. Helstrom, Statistical Theory of Signal Detection, 2nd ed., Pergamon Press, 1975.
- H. V. Poor, An Introduction to Signal Detection and Estimation, Springer-Verlag, 1988
COURSE COORDINATOR: C.C. Lee
INSTRUCTOR: C.C. Lee
PREREQUISITES: Fourier transform, signals and systems, probabilities and random processes.
DETAILED COURSE TOPICS
1. Problem description and review of mathematical background
2. Testing of statistical hypotheses: Baysian, Minimax, and Neyman-Pearson optimum criteria
3. Locally optimum procedures and detector comparison techniques
4. (Continuous-time) detection of a known signal
5. Detection of signal with random parameters
6. Multiple pulse detection of radar signals
7. Nonparametric detection
8. Estimation of signal parameters
9. Special topics (as time permits); Sequential Tests, Distributed Detection, Mathematical Reasoning
GRADES
- Homework 50%
- Final Exam – 50%
COURSE OBJECTIVES