University of Illinois at Chicago, ECE

Spring 2011

Instructor: Natasha Devroye, devroye@ece.uic.edu

Course coordinates: Tuesday, Thursday from 2-3:15pm in Lincoln Hall 103.

Office hours: Tuesdays from 3:30-4:30pm and Thursdays 5-6pm in SEO 1039, or by appointment

Harry L. Van Trees, Detection, Estimation, and Modulation Theory, Part I, II, III, IV

H. Vincent Poor, Introduction to Signal Detection and Estimation

Louis L. Scharf and Cedric Demeure, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis

Carl Helstrom, Elements of Signal Detection and Estimation. It's out of print, so here's my pdf copy.

A nice survey on the EM algorithm

General Minimum Variance Unbiased Estimation, Ch.2+Ch.3, and Chapter 5 notes

Cramer-Rao Lower Bound, Ch.3

Linear Models+Unbiased Estimators, Ch.4 and Ch. 6 notes

Maximum Likelihood Estimation, Ch.7 notes

Least squares estimation, Ch.8 notes

Bayesian Estimation, select Ch.10-12 notes

Kalman filtering, select Ch.12-13 notes

Statistical Detection Theory, Ch.3 notes

Deterministic Signals, Ch.4< notes

Random Signals, Ch.5 notes

Statistical Detection Theory 2, Ch.6 notes

HW1: out 1/13 due 1/20 -- Book 1, problems 2.1, 2.3, 2.8, 2.9, Solutions

Hw2: out 1/20 due 1/27 -- Book 1, problems 3.3, 3.11, 3.15 Solutions

HW3: out 1/27 due 2/3 -- Book 1, problem 4.6, 4.13, 5.3, 5.9 Solutions

HW4: out 2/3, due 2/10 -- Book 1, problem 6.7, 6.9, 7.3, 7.14 Solutions

HW5: out 2/17, due 2/24 -- Book 1, problem 7.18, 7.20, 8.5, 8.10 Solutions

HW6: out 2/24, due 3/3 -- Book 1, problem 8.20, 8.27, 10.3 Solutions

HW7: out 3/3, due 3/10 -- Book 1, problem 11.3, 12.1, 12.11 Solutions

HW8: out 3/10, due 3/17 -- Book 1, problem 13.4, 13.12, 13.15 Solutions

HW9: out 3/31, due 4/7 -- Book 2, problem 3.4, 3.6, 3.12, 3.18 Solutions

HW10: out 4/7, due 4/14 -- Book 2, problem 4.6, 4.10, 4.19, 4.24 Solutions

HW11: out 4/14, due 4/21 -- Book 2, problem 5.14, 5.17, 6.2 Solutions

Practice midterm1 2009, Midterm 1 2009, Pratice midterm2 2009, Midterm 2 2009, Practice final 2009

The project will consist of 3 parts: 1) a 5-8 pages, single spaced latex 11-12pt report, 2) a live demo, of your implementation, or comparison between different implementations/techniques, and 3) a presentation in front of the class which will introduce the class to your selected topic through slides and the live demo, which should be 15 minutes long. More details will be given during the term.

1/11: First class, introduction. Introduction slides

1/13: Minimum variance unbiased estimation and the Cramer-Rao Lower Bound.

1/18: Cramer-Rao Lower Bound.

1/20: Cramer-Rao Lower Bound, Linear Model.

1/25: Linear Model.

1/27: General MVUE.

1/28: MAKEUP lecture for 2/8, to be held in LH 103 from 4-5pm.

2/1: General MVUE.

2/3: Best Linear Unbiased Estimator (BLUE).

2/8: Devroye out of town, class cancelled.

2/10: Midterm 1.

2/15: Maximum likelihood estimation (MLE).

2/17: MLE.

2/22: MLE

2/24: MLE, Least Squares Estimation. Deadline to select project topic

3/1:

3/3: Selected topics Ch.10-12, Bayesian estimation.

3/8: Bayesian estimation.

3/10: Kalman filtering.

3/15: Midterm 2.

3/17: Detection theory - intro to statistical detection theory.

3/22: Spring Break, no class

3/24: Spring Break, no class

3/29: Detection theory, chapter 3 continued. Project outline due.

3/31: Detection theory - Deterministic Signals Ch 4.

4/5: Ch.4 on Detection of Deterministic Signals in Gaussian noise continued.

4/7: Ch.5 on Detecion of Random Signals in Gaussian noise.

4/12: Ch 5 + Ch.6 on Statistical Decision Theory II.

4/14: Ch.6 on Statistical Decision Theory II.

4/19: Buffer. Written project report due.

4/21: Last class Review notes.

4/26: Project presentations.

4/28: Project presentations.

Final exam week 5/2 - 5/6