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)
)
