ECE 531: Detection and Estimation Theory

Prof. Mojtaba Soltanalian, UIC

Popular science description: here and here!

Lectures & office hours

Lectures are given Tuesdays and Thursdays, 2:00-3:15pm in Lecture Center Building A- A007

Office hours: Thursdays 3:45-5:00pm, SEO 1031

Textbook and optional references

* Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory, by Steven M. Kay, Prentice Hall, 1993, and (possibly)

* Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, by Steven M. Kay, Prentice Hall 1998,

available in hard copy at the UIC Bookstore.

The following online resources will be partly used in the class:

http://user.it.uu.se/~ps/sysidbook.pdf
http://user.it.uu.se/~ps/SAS-new.pdf

Other useful references:

- Harry L. Van Trees, Detection, Estimation, and Modulation Theory,

- 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.

Lectures

- The slides can be found here.

Date

To be covered

Further info.

1/10

Introduction to ECE 531

1/12

Basics- A Refresher

1/17

Modeling for Estimation / Spectral Estimation

1/19

Minimum Variance Unbiased Estimator (MVUE)

1/24

Cramer-Rao Lower Bound (CRLB)

1/26

CRLB / Linear Model

1/31

General MVUE

2/2

General MVUE

2/7

Best Linear Unbiased Estimator (BLUE)

2/9

Review Class

2/14

Midterm 1

2/16

Maximum Likelihood (ML) Estimation

2/21

ML Estimation
(P) Paulito Mendoza: Gaussian Assumption Leads to the Largest Cramer-Rao Bound

2/23

Least Squares Estimation
(P) Nishat Anjum Khan: Kalman Filtering for State-of-Charge Estimation

2/28

Bayesian Estimation
(P) Matthew Klug: Image Change Detection Algorithms

3/2

Kalman Filtering
(P) Shahin Khobahi: Object Tracking and Kalman Filtering

3/7

No Classes

 

3/9

No Classes

 

3/14

Review Class

Deadline to select project topic

3/16

Midterm 2

 

3/21

Spring Break, No Classes

3/23

Spring Break, No Classes

3/28

Detection Theory- Preliminaries
(P) Sara Shahi: Mutual Information and Minimum Mean Square Error in Gaussian Channels

3/30

Detection Theory- Deterministic Signals
(P) Prabhu Annabathula: Sensor Array ML Estimation and CRB for Narrowband Signals

4/4

Detection Theory- Random Signals
(P) Jacob Miller: Exact and Approximate Solutions of Source Localization Problems

4/6

Optimization for Detection and Estimation
(P) Steven Sandoval: Robust Kalman Filtering for Satellite Attitude Estimation (P) Brook Feyissa: Geometry of the Cramer-Rao Bound

4/11

Selected Topics
(P) Irfan Feroz: The Expectation-Maximization Algorithm --------- (P) Aria Ameri: Cramer-Rao Bounds for Low-Rank Tensor Decomposition --- (P) Mohammadreza Mousaei: Training Signal Design for Correlated Massive MIMO

4/13

Project Presentations

4/18

Project Presentations

4/20

Review Class

4/25

Project Presentations

4/27

Project Presentations

Final Exam Week 5/1 - 5/5


Course requirements and grading

Paper Presentations & Discussions 10%

Computer Project 15%

Homework 15%

Midterm exams 15% each

Final exam 30%

 

* An extra paper presentation for 10% bonus point may be accommodated. Prior consent from the instructor is required.

* These weights are approximate and may be subject to change.

* Late homework will not be accepted.

 

Homeworks (updated 4-14-2017)

HW1: out 2/3 due 2/9 -- Estimation Book, problems 1.4, 2.9, 3.10, 3.11, 3.12

HW2: out 2/3 due 2/14 -- Estimation Book, problems 4.1, 4.2, 5.9, 6.6, 6.13

HW3: out 4/14 due 4/20 -- Detection Book, problems 3.8, 3.14, 4.1, 4.6

HW4: out 4/14 due 4/27 -- Detection Book, problems 4.10, 5.12, 5.13

 

Paper presentations

The presentations should be around 20-25 mins.

Some suggested papers can be found here.

 

Note

Students who wish to observe religious holidays should notify the instructor by the tenth day of the semester of the date when they will be absent unless the religious holiday is observed on or before the tenth day of the semester. In such cases, the students should notify the instructor at least five days in advance of the date when he/she will be absent. Every reasonable effort will be made to honor the request.

Academic dishonesty by students including plagiarism will result in appropriate disciplinary action. Intentional use or attempt to use unauthorized assistance, materials, information, or people in any examination, quiz, or assignment may lead to penalties such as a failing grade. College of Engineering and University guidelines will be followed.