# ECE 7251: Signal Detection and Estimation

Georgia Institute of Technology

Spring 2002

**Instructor:** Prof. Aaron Lanterman

**Office:** GCATT 334B

**Phone:** 404-385-2548

**E-mail:** lanterma@ece.gatech.edu

**Course website**: users.ece.gatech.edu/~lanterma/ece7251

**When and where:** MWF, 12:05-12:55,

212 Engineering Science and Mechanics Building

# Syllabus

# News

- The first quiz has been postponed to Monday, Feb. 11.

# Lectures, Suggested Readings, and Suggested Problems

- Lecture 1: Introduction (1/4/01)

(ppt)

(pdf) - Lecture 2: Sufficient Statistics and Exponential Families (1/7/01)

(ppt)

(pdf) (Poor, pp. 158-160, 164; Hero, pp. 24-30; some lecture

examples taken from pp. 30-33 of Srinath)

**Suggested problems:**Hero, Sec. 3.5, #5, #6 (parts a and b only) - Lecture 3: Introduction to Bayesian Estimation (1/9/01)

(ppt)

(pdf) (Poor, pp. 142-147)

(Poor, pp. 142-147; Hero pp.

32-38) - Lecture 4: Examples of Bayesian Estimation (1/11/01)

(whiteboard)

(Poor, pp. 147-152,**pay particular attention to Example IV.B.2**;

Hero, pp. 38-42)

**Suggested problems:**Poor, Sec. IV.F: #1, #7 - Lecture 5: More Examples and Properties

of Bayesian Estimation

(1/14/01)

(whiteboard)

(Hero, pp. 42-46)

**Suggested problem:**Poor, Sec. IV. F: #25 (parts a and b only)

**Highly suggested problem:**Try to derive the CME,

CmE, and MAP estimators

on pp. 43-44 of Hero (good practice with erf functions; you may need to do

integration by parts) - Lecture 6: The Orthogonality Principle in MMSE Estimation (1/16/01)

(ppt)

(pdf)

(Poor, pp. 221-229; Hero, pp. 82-96)

**Suggested problems:**

Three interesting MMSE problems with solutions

(PDF), Scan of an old UIUC exam problem

(This one is interesting since it shows how the orthogonality principle

is useful for things beyond computing linear MMSE. Here, you compute a

quadratic MMSE!) - Lecture 7: Examples of Linear and Nonlinear MMSE Estimation (1/18/01)
- Lecture 8: Nonrandom Parameter Estimation (1/23/01)

(ppt)

(pdf)

(Poor, pp. 173-185; the discussion on p. 179 and continuing on to the

top of p. 180 is particularly enlightening; Hero, pp. 51-60, pp. 70-76) - Lecture 9: The Cramer-Rao Bound (1/25/01)

(ppt)

(pdf)

(Poor, pp. 167-173, pp. 185-186; Hero, pp. 60-70)

**Suggested problems:**Poor, Sec. IV. F: #15, #25 (now try parts c and d) - Lecture 10: Estimation Under Additive Gaussian Noise (a.k.a. Least

Squares Solutions) (1/28/01)

(ppt)

(pdf)

(Poor, pp. 155-157) - Lecture 11: Examples with Non-Gaussian Data, Part I
- Lecture 12: Examples with Non-Gaussian Data, Part II
- Lecture 13:”The” Expectation-Maximization Algorithm

(Basic Formulation and Simple Example) (2/4/02)

(ppt)

(pdf)

)