Course Details

ELE 771 Spectral Estimation
2019-2020 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 ( in real-time and displayed here. Please check the appropriate page on the original site against any technical problems.


Course Name Code Semester Theory
Credit ECTS
SPECTRAL ESTIMATION ELE771 Any Semester/Year 3 0 3 10
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Problem Solving
Instructor (s)Department Faculty 
Course objectiveSuccessful students are expected to gain knowledge of basic spectral estimation methods used for analysis of stochastic processes and signals.  
Learning outcomes
  1. A student completing the course successfully
  2. L.O.1. Recognizes spcetral estimation problems,
  3. L.O.2. Models the encountered problems in suitable forms,
  4. L.O.3. Knows which algorithms can be used to solve the problem established, knows advantages and disadvantages of these algorithms,
  5. L.O.4. Applies the techniques and algorithms learnt in the class in projects,
  6. L.O.5. Has the adequate knowledge to follow and understand advanced up-to-date algorithms.
Course ContentReview of probability and stochastic processes. Periodogram and Blackman-Tukey spectral estimation. Autoregressive (AR), moving average (MA) and autoregressive-moving average (ARMA) spectral estimation. Minimum-variance spectral estimation. Sinusoidal Parameter Est. Bispectrum and polyspectrum. Spectral estimation of Nonstationary signals. Array processing. 
References1. Spectral Analysis of Signals, P. Stoica and R. Moses. Pearson.
2. Modern Spectral Estimation, S. Kay. Prectice-Hall.
3. Digital Spectral Analysis, L. Marple. Prentice-Hall.
4. Lecture Notes.

Course outline weekly

Week 1Review Of Probability.
Week 2Power Spectral Density.
Week 3Periodogram , Avg. Periodogram, Blackman-Tukey, Welch Methods.
Week 4Parametric Modelling, Linear Prediction.
Week 5Levinson Algorithm, Maximum Entropy.
Week 6Sinusoids in Noise.
Week 7Autocorrelation, Covariance, Modified Cov. Methods, Burg Algoritm
Week 8Durbin Method(MA) , ARMA spectral Estimation.
Week 9Term Exam.
Week 10Model Order Estimation, Minimum Variance Sp. Est., Filterbank.
Week 11Sinusoidal Parameter Est., Pisarenko, MUSIC, ESPRIT.
Week 12Higher Order Spcetrum (Bispectrum)
Week 13Nonstationary Spectral Estimation (Wigner D., Wavelet Tr., Evolutionary Sp.)
Week 14Array Processing.
Week 15Final Exam.
Week 16Final Exam.

Assesment methods

Course activitiesNumberPercentage
Field activities00
Specific practical training00
Final exam150
Percentage of semester activities contributing grade succes5050
Percentage of final exam contributing grade succes5050

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)149126
Presentation / Seminar Preparation000
Homework assignment8648
Midterms (Study duration)13030
Final Exam (Study duration) 14040
Total Workload39103301

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
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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