E1 244: Detection and Estimation Theory
Logistics
Instructor: Chandra R. Murthy (cmurthy at ece)
Class hours: MWF 11am-12:30pm, Room L5 in the Central Lecture Hall Complex (near the GATE office).
TA: Bharath B. N. (bharath at ece)
Textbooks:
1. H. V. Poor, An Introduction to Signal Detection and Estimation, 2nd Ed., Springer-Verlag, 1994.
2. H. L. Van Trees, Detection, Estimation and Modulation Theory, Parts 1 and 2, John Wiley Inter-Science.
3. E. L. Lehman, Testing Statistical Hypothesis, John Wiley, 1986.
4. M. D. Srinath, P. K. Rajasekaran and R. Vishwanathan, An Introduction to Statistical Signal Processing with Applications, Prentice-Hall, 1996.
5. To review probability: A. Papoulis, Probability, random variables, and stochastic processes, McGraw-Hill International Edition.
Prerequisites: Random processes (E2-202 or equivalent).
Syllabus:
| S. No. | Topic | Num. Lectures |
|---|---|---|
| 1 | Background and probability review | 2 |
| 2 | Bayesian, Neyman-Pearson & Minimax detection | 2 |
| 3 | Composite hypothesis testing, Generalized LRT | 1 |
| 4 | Sequential and distributed detection | 2 |
| 5 | Performance analysis | 1 |
| 6 | Nonparametric detection | 1 |
| 7 | Signal detection in continuous time, KL theorem | 1 |
| 8 | Detection in Gaussian noise | 2 |
| 9 | Estimation theory: Bayesian, MMSE and MAP estimation | 2 |
| 10 | Fischer-Neyman factorization theorem, Rao-Blackwell theorem | 2 |
| 11 | Maximum-Likelihood estimation | 2 |
| 12 | Exponential families and the Cramer-Rao Bound | 2 |
| 13 | Consistency, efficiency and asymptotics | 2 |
| 14 | Kalman filtering | 2 |
| 15 | Linear estimation of signals, Weiner filtering | 2 |
| Total | 26 |
Grading
Homeworks: 10%
Midterm 1: Feb. 12, 2010, in class: 25%. Click here for the solution. Thanks to Bharath for preparing the solution!
Midterm 2: Mar. 12, 2010, in class: 25%. Click here for the solution. Thanks to Prof. Srinivasan for help with preparing the test and the solution!
Final: Apr. 27, 2010, 9am – 12pm: 40%
Homeworks
Homeworks are posted here.