Prof. Mojtaba
Soltanalian, UIC
Popular science description: here and here!
Lectures are given Tuesdays and Thursdays, 2:003:15pm in Lecture Center Building A A007
Office hours: Thursdays 3:455:00pm, SEO 1031
* 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/SASnew.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.
 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 
CramerRao 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 CramerRao Bound 
2/23 
Least Squares Estimation 
(P) Nishat Anjum Khan: Kalman Filtering for StateofCharge 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 CramerRao Bound 
4/11 
Selected Topics 
(P) Irfan Feroz: The ExpectationMaximization Algorithm  (P) Aria Ameri: CramerRao Bounds for LowRank 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 
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 4142017)
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 2025 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.