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: 
Asst.
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, Homeworks: %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 (9^{th} Feb.) 
Introduction 
ppt
(presentation) 
WeekII (16^{th} Feb.) 
Review of Random
Variables and Random Processes 
Matlab
Exercise for random variables 
WeekIII(23^{rd} Feb.) 
Review of Random Variables and Random Processes 
There
will be no lessons on 23rd February. HW1 is submitted. 
WeekIV (2^{nd} Mar.) 
Review of Random
Variables and Random Processes 
HW1
(due:9^{th} March) 
WeekV (9^{th} Mar.) 
Minimum variance unbiased estimation (MVU) 

WeekVI (16^{th} Mar.) 
Cramer Rao Lower Bound (CRLB), Linear
Models 
HW2
(Due:30^{th} March) (3.3/3.4/3.5/3.6) 
WeekVII (23^{rd}
Mar.) 
Linear Models, Best Linear Models Note: Please visit Projects
page! (Project proposal deadline: 6th April ) 

WeekVIII (30^{th}
Mar.) 
Maximum Likelihood
Estimation 

WeekIX (6^{th} April) 
Least Square Estimation 

WeekX (13^{th} April) 
Midterm Exam (in
class) 
in E3 at 10:00 am 
WeekXI (20^{th}
April) 
Bayesian estimation Take home exam (midtermII) 
(RSS data generation code example) 
WeekXII (27th April) 
Bayesian Estimation 


Important Dates: ·
Extended deadline for take home exam1: 5^{th} May 2017 

WeekXIII (4th May) 
Wiener and Kalman Filtering 
Lecture notes 
Week –XIV (11th May) 
Kalman Filtering 



