EEM547
Fundamentals of Detection and Estimation
The objective of this course is to present the theory
and applications of statistical signal processing to detection and estimation of
signal parameters in noise. A solid background in signal processing,
probability and random processes, and linear and matrix algebra is needed.
Instructor: 
Office: 
email: 
Assoc.
Prof. Dr. Tansu Filik 
EEM  214 
tansufilik@anadolu.edu.tr 
Course Outline:
1.
Introduction
to signal detection and estimation 
2.
Review
of the theory of random variables and random signals 
3.
Classical
estimation theory, general minimum variance unbiased (MVU) estimation 
4.
CramerRao
Lower Bound (CRLB) 
5.
Linear
models and best linear unbiased estimators 
6.
Maximum
likelihood estimation 
7.
Least
squares estimation 
8.
Bayesian
estimation 
9.
Wiener
and Kalman filtering 
10.
Classical
detection theory 
11.
Detection
in Gaussian and nonGaussian noise 
Grading:
MidtermI (take home): %15, MidtermII: %20 (in class), Final: %35, Homework’s:
%10,
Project: %20
Textbooks:
1) Steven
M. Kay, Fundamentals
of Statistical Signal Processing: Estimation Theory, Prentice Hall,
2) Steven
M. Kay, Fundamentals
of Statistical Signal Processing: Detection Theory, Prentice Hall,
3) Hary L. Van Trees, Detection,
Estimation, and Modulation Theory, Part 1, WileyInterscience
Reference Books:
·
Vincent
Poor, An Introduction to Signal Detection
and Estimation, Springer,
·
Charles
W. Therrien, Discrete
Random Signals and Statistical Signal Processing, Prentice Hall,
·
Monson
H. Hayes, Statistical Digital Signal
Processing and Modelling, Wiley
Lectures 
Subject 
Links 

Background materials: ·
book, An
Introduction to Statistical Signal Processing ·
(fast
review for discrete random processes) ·
book,
Intuitive Probability and Random Processes Using MATLAB) 


Notes on Gaussian distribution by Dr. C. Candan: 

WeekI (8^{th} Feb.) 
Introduction, ppt (presentation) 
There will be no
lecture on 8^{th} February Lectures will start on
15^{th} February 
WeekII (15^{th} Feb.) 
Introduction, Review of Random
Variables and Random Processes 

WeekIII (22^{rd}
Feb.) 
Review of Random Variables and Random Processes 

WeekIV (1^{st} Mar.) 
Minimum
variance unbiased (MVU) estimation 
HW1
is submitted 
WeekV (8^{th} Mar.) 
CramerRao Lower Bound (CRLB) 
HW2: 3.3, 3.5, 3.6, 3.11 (the questions
from textbook1) 
WeekVI (15^{th} Mar.) 
Linear
models and best linear unbiased estimators 

WeekVII (22^{nd} Mar.) 
Best linear unbiased
estimators (BLUE) 
HW3: 4.1, 4.2, 4.4 (from textbook1) Due: 5^{th} April 
WeekVIII (29^{th} Mar.) 
Midterm Examination (in class – 29^{th}
March at 10:00 am) 
1^{st} Midterm
exam will be held on 29^{th} March in class
at 10:00 am. 
important
announcement 
You should
propose a project due to April 5.
Please visit Project
page for details. 

WeekIX (5^{th} April) 
Maximum
likelihood estimation, Least Square Estimation 
HW4:
7.10, 7.13, 8.3, 8.5 (from textbook1) Due: 12^{th} April 
WeekX (12^{th}
April) 
Least Square Estimation, Classical detection theory 

WeekXI(19^{th}
April) 
Classical
detection theory, Bayesian Estimation 

WeekXII (26^{th}
April) 
Wiener Filtering 

WeekXIII (3^{rd}
May) 

There will be no
lecture on 3^{rd} May 
WeekXIV (10^{th}
May) 
Kalman
Filtering 

WeekXV (17^{th} May) 
Submit project final report and present
your work in class ( Present your project ( 
Take Home Exam2 