ACADEMICS
Course Detail

ELE 736 Detection and Estimation Theory
2017-2018 Fall term information

The course is not open this term

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.

ELE736 - DETECTION and ESTIMATION THEORY

Course Name Code Semester Theory
(hours/week)
Application
(hours/week)
Credit ECTS
DETECTION and ESTIMATION THEORY ELE736 Any Semester/Year 3 0 3 10
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)1410140
Presentation / Seminar Preparation000
Project000
Homework assignment6530
Midterms (Study duration)14242
Final Exam (Study duration) 14646
Total Workload36106256

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Has highest level of knowledge in certain areas of Electrical and Electronics Engineering.   X 
2. Has knowledge, skills and and competence to develop novel approaches in science and technology.   X 
3. Follows the scientific literature, and the developments in his/her field, critically analyze, synthesize, interpret and apply them effectively in his/her research.   X 
4. Can independently carry out all stages of a novel research project.  X  
5. Designs, plans and manages novel research projects; can lead multidisiplinary projects.  X  
6. Contributes to the science and technology literature.  X  
7. Can present his/her ideas and works in written and oral forms effectively; in Turkish or English.X    
8. Is aware of his/her social responsibilities, evaluates scientific and technological developments with impartiality and ethical responsibility and disseminates them.X    

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

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