ECE 531: Detection and Estimation Theory

Prof. Mojtaba Soltanalian, UIC

Course webpage: http://msol.people.uic.edu/ECE531

Popular science description: here and here!

Lectures & office hours

Lectures are given Tuesdays and Thursdays, 2:00-3:15pm in 320 LH

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.

1/16

Introduction to ECE 531

1/18

Basics- A Refresher

1/23

Modeling for Estimation / Spectral Estimation

1/25

Minimum Variance Unbiased Estimator (MVUE)

1/30

Cramer-Rao Lower Bound (CRLB)
(P) Kenneth Palacio Baus: Image Change Detection Algorithms

2/1

CRLB / Linear Model

2/6

General MVUE

2/8

General MVUE
(P) Amruthavarshini Ramesh: Shredded Signal Reconstruction via Sparsity Enhancement

2/13

Best Linear Unbiased Estimator (BLUE)

2/15

Review Class

2/20

Midterm 1

2/22

Maximum Likelihood (ML) Estimation
(P) Patrick Dahl: Cramer-Rao Bound for Distributed Positioning in Sensor Networks

2/27

ML Estimation
(P) Kenneth Palacio Baus: The Expectation-Maximization Algorithm

3/1

Least Squares Estimation

3/6

Bayesian Estimation
(P) Diaa Badawi: The Gaussian Data Assumption Leads to the Largest Cramer-Rao Bound

3/8

Kalman Filtering
(P) Tumin Wu: Estimation in Wireless Sensor Networks

3/13

Midterm 2

 

3/15

No Classes

 

3/20

Detection Theory- Preliminaries
(P) Zhizhen Shen: Mean Squared Error: Love It or Leave It?

3/22

Detection Theory- Deterministic Signals
(P) Zihao Gong: Minimum Variance in Biased Estimation

3/27

Spring Break, No Classes

3/29

Spring Break, No Classes

4/3

Detection Theory- Random Signals

Deadline to select project topic; (P) Yohannes Azeze: ML and MAP Direction-of-Arrival Estimation

4/5

Detection Theory- Selected Topics
(P) Yishu Bai: ML Estimation and CRB for Narrowband AR Signals

4/10

Optimization for Detection and Estimation
(P) Yubo Fan: Distributed Least Squares Estimation Over Networks

4/12

Review Class
(P) Yizhou Zhao: State-of-Charge Estimation for Supercapacitors; (P) Qu Yongquan: Cramer-Rao Bound Analog of Bayes' Rule; (P) Amruthavarshini Ramesh: Optimized Unbiased Sampling for Monte Carlo Simulations

4/17

No Classes

4/19

No Classes

4/24

Project Presentations

4/26

Project Presentations

5/1

Project Presentations

5/3

Project Presentations

Final Exam Week 5/7 - 5/11


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.

 

Homework (updated 4-2-2018)

HW1: out 2/7 due 2/15 -- Estimation Book, problems 1.4, 2.9, 2.11, 3.10, 3.11, 3.12

HW2: out 2/7 due 2/20 -- Estimation Book, problems 4.1, 4.2, 5.9, 6.6, 6.13

HW3: out 4/2 due 4/12 -- Detection Book, problems 3.8, 3.14, 4.1, 4.6

HW4: out 4/2 due 4/26 -- 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.