ACADEMICS
Course Detail

ELE 636 Detection and Estimation Theory
2016-2017 Spring term information

The course is open this term
Section:
Supervisor(s):Dr. Mücahit K. Üner
PlaceDayHours
SSThursday13:00 - 15:45

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University (http://ects.hacettepe.edu.tr) in real-time and displayed here. Please check the appropriate page on the original site against any technical problems.

ELE636 - DETECTION and ESTIMATION THEORY

Course Name Code Semester Theory
(hours/week)
Application
(hours/week)
Credit ECTS
DETECTION and ESTIMATION THEORY ELE636 Any Semester/Year 3 0 3 8
Prerequisite(s)None. Students are expected to have taken ELE 324, ELE 425 courses.
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Problem Solving
 
Instructor (s)Assist.Prof.Dr. Mücahit Üner 
Course objectiveThe objective of the course is to provide a good understanding of detection and estimation theory which represents a combination of the classical techniques of statistical inference and the random process characterization of communication, radar, sonar, and other modern data processing systems 
Learning outcomes
  1. State Binary and M-ary Hypotheses Testing
  2. Evaluate the performance of decision making and estimation systems
  3. Derive Cramer-Rao bound
  4. Find the maximum likelihood, maximum a posteriori probability and least squares estimates of a parameter
  5. Perform Karhunen-Loeve expansion
Course ContentClassical Detection and Estimation Theory :
- Binary Hypothesis Testing
- Optimum Decision Criteria : Bayes, Neyman-Pearson, Minimax
- Decision Performance : Receiver Operating Characteristic
- M-ary Hypotheses Testing
Estimation Theory :
- Random parameter estimation : MS, MAP estimators
- Nonrandom and unknown parameter estimation : ML estimator
- Cramer-Rao lower bound
- Composite Hypotheses
- The general Gaussian problem
Representation of Random Processes:
- Orthogonal representation of signals
- Random process characterization
- White noise processes
Detection of continuous signals
- Detection of known signals in white Gaussian noise
 
ReferencesVan Trees, Detection, Estimation, and Modulation Theory, Part I, Wiley, 2001.
Shanmugan and Breipohl, Random Signals, Wiley, 1988.
H.V. Poor, An Introduction to Signal Detection and Estimation, Fall/ Springer, New York, 1994.
C.W. Helstrom, Elements of Signal Detection and Estimation, Prentice Hall, 1995.
 

Course outline weekly

WeeksTopics
Week 1Binary Hypothesis Testing
Week 2Optimum Decision Criteria
Week 3Decision Performance
Week 4M-ary Hypotheses Testing
Week 5Random parameter estimation
Week 6Nonrandom parameter estimation
Week 7Cramer-Rao inequality
Week 8Composite Hypotheses
Week 9The general Gaussian problem
Week 10Midterm Exam
Week 11Orthogonal representation of signals
Week 12Representation of Random Processes
Week 13White noise processes
Week 14Detection of known signals in white Gaussian noise
Week 15Preparation Week for Final Exams
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments615
Presentation00
Project00
Seminar00
Midterms140
Final exam145
Total100
Percentage of semester activities contributing grade succes055
Percentage of final exam contributing grade succes045
Total100

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14798
Presentation / Seminar Preparation000
Project000
Homework assignment6530
Midterms (Study duration)13232
Final Exam (Study duration) 13838
Total Workload3685200

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.   X 
2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering.   X 
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.   X 
4. Designs and runs research projects, analyzes and interprets the results.   X 
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.  X  
6. Produces novel solutions for problems.  X  
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.   X 
8. Follows technological developments, improves him/herself , easily adapts to new conditions.   X  
9. Is aware of ethical, social and environmental impacts of his/her work.X    
10. Can present his/her ideas and works in written and oral form effectively; uses English effectivelyX    

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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