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. and Nirmal Shende
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 | Estimation theory: Bayesian, MMSE and MAP estimation | 2 |
2 | Fischer-Neyman factorization theorem, Rao-Blackwell theorem | 2 |
3 | Maximum-Likelihood estimation | 2 |
4 | Exponential families and the Cramer-Rao Bound | 2 |
5 | Consistency, efficiency and asymptotics | 2 |
6 | Kalman filtering | 2 |
7 | Linear estimation of signals, Weiner filtering | 2 |
8 | Bayesian, Neyman-Pearson & Minimax detection | 2 |
9 | Composite hypothesis testing, Generalized LRT | 2 |
10 | Sequential and distributed detection | 4 |
11 | Performance analysis | 2 |
12 | Signal detection in continuous time, KL theorem | 2 |
13 | Detection in Gaussian noise | 2 |
Total | 28 |
Grading
Midterm 1: Feb. 11, 2011, in class: 25%. For solutions, click here.
Midterm 2: Mar. 14, 2011, in class: 25%. For solutions, click here.
Final: Apr. 26, 2011, 9am – 12pm: 50%
Homeworks
Homeworks are posted here.