ECE 6540 - Estimation Theory

Electrical and Computer Engineering Department


Course Info

Lectures: WF 1:25 PM - 2:45 PM in WEB L120

Instructor: Tolga Tasdizen
Email: tolga@sci.utah.edu
Office: WEB 3887
Office Hours: noon - 1:20 pm Fridays. Also with appointment outside regular office hours.

Required Textbook: Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory
                                 Steven M. Kay
                                 Prentice Hall, Upper Saddle River, NJ 07458
                                 ISBN: 0-13-345711-7

Prerequisite: ECE 5510 and 5530

Detailed course information and syllabus

Important Notices

Read Chapters 1 & 2 from textbook
Read Chapter 3
Read Chapter 4
Read Chapter 5
Read Chapters 6 and 7
Read Chapter 8
Read Chapter 9
Read Chapter 10
Read Chapter 11
Read Chapter 12

Lecture Notes

* Introduction
* Minimum Variance Unbiased Estimation
* Cramer-Rao Lower Bound (scalar case)
(New) Extra Examples for Chapter 3
Cramer-Rao Lower Bound (vector case)
Linear Models
Colored Noise
Sufficient Statistics
Best linear unbiased estimation
* Maximum Likelihood Estimation
* Monte Carlo Matlab script for class example
Expectation Maximization
Least Squares Part I
* Least Squares Part II
* MATLAB examples: Least squares model order, Sequential Least Squares, Sequential Least Squares with update rule
Method of moments
* Bayesian Estimation Introduction
Gaussian Prior
Bayesian MMSE estimator continued
Maximum a posteriori estimation
LMMSE estimator and Sequential LMMSE estimator

Exam material ends here

Wiener Filtering MATLAB: AR-1 process, Wiener Filter for AR-1 process, Wiener Filter for image, Image Blurring
Kalman Filtering
* MATLAB: Gauss-Markov process, KalmanFilter
Vector state/observation Kalman Filter, MATLAB: demo1, demo2

Assignments

(8/26) Assignment 1 due 9/9 solutions
(9/9) Assignment 2 due 9/23 solutions
(10/21) Assignment 3 due 11/2 solutions
(11/1) Assignment 4 due 11/9 solutions
(11/17) Assignment 5 due 12/2 solutions

Exams and solutions

Practice exam midterm 1

Midterm 1 solution

Midterm 2 topics:  Chapter 6 except 6.6, Chapter 7 except 7.10, Chapter 8 except 8.8-8.10, Chapter 9 except 9.5-9.6

Practice exam midterm 2 and solution

Midterm 2 solution

Final exam topics: Chapter 10, Chapter 11 Sections 1-5, Chapter 12 Sections 1-4 and Section 6

Practice exam final and solution