| | Standard Course Syllabus | Course Supervisor | Date of Approval |
| | Dept. of Electrical and Computer Engineering | Moses | February 17, 1999 |
| | 806 | Signal Detection and Estimation |
| | 2. | CATALOG DESCRIPTION |
| | Binary and M-ary decision theory; Bayes, minimax, ideal, and Neyman-Pearson detectors; maximum likelihood and |
| | maximum a posteriori estimation; and receiver operating characteristics. |
| | Quarters of Offering | Credits | | Level | Class Meeting |
| | Sp Qtr. | 3 | G | 3 cl. |
| | Course Prerequisites |
| | Prereq: 805. |
| | 3. | PREREQUISITES BY TOPIC |
| | Random variables, probability density functions, expectation operator, conditional expectation and conditional probability, |
| | statistical independence, random processes, convergence of random sequences, linear algebra. |
| | Courses that require this as a direct prerequisite |
| | 807, 808 |
| | 4. | Text(s) and Other Course Materials | Author(s) | Publisher |
| | An Introduction to Signal Detection and Estimation, 2nd Ed. | H. V. Poor | Springer |
| | References (supplemental reading) |
| | [1] Communications and Networks: A Survey of Recent Advances, Blake \& Poor (Springer- Verlag: 1986) |
| | [2] Abstract Inference, Grenander (Wiley: 1981) |
| | [3] Elements of Signal Detection and Estimation, Helstrom (Prentice Hall: 1995) |
| | [4] Statistical Theory of Signal Detection, 2nd ed., Helstrom (Pergamon: 1968) |
| | [5] Testing Statistical Hypotheses, Lehmann (Wiley: 1986) |
| | [6] Theory of Point Estimation, Lehmann (Wiley: 1983) |
| | [7] Detection, Estimation, and Modulation Theory, Van Trees (Wiley: 1971) |
| | [8] Probability and Random Processes, Viniotis (McGraw Hill:1998) |
| | 5. | COURSE OBJECTIVES |
| | 1. Students learn the theory of likelihood-ratio based hypothesis testing and signal detection in noise. (Criterion 3(a)) |
| | 2. Students learn the theory of optimal parameter estimation; properties of estimators; and learn the tools for analysis of the |
| | efficacy of parameter estimators. (Criterion 3(a)) |
| | 3. Students learn to design and analyze optimal and sub-optimal detection and estimation algorithms under realistic |
| | conditions. (Criteria 3(b),(c),(e),(g),(k)) |
| | 6. | TOPICS AND (# OF LECTURES) |
| | Basic Hypothesis Testing: Bayesian Hypothesis Testing, Minimax Hypothesis Testing, Neyman-Pearson Hypothesis |
| | Testing, Composite Hypothesis Testing (9) |
| | Signal Detection in Discrete Time: Deterministic Signals, Stochastic Signals (8) |
| | Basic Parameter Estimation: Bayesian Parameter Estimation, Minimum-Variance Unbiased Estimators, Properties of |
| | Estimators, Maximum-Likelihood Parameter Estimation (14) |
| | 7. | CLASS MEETING PATTERN | (For example, "3cl." means 3 48-min classes per week.) |
| | 3 cl. |
| | Thursday, August 14, 2008 09:22 AM |
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