Course Details

ELE 671 Spectral Estimation
2020-2021 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 data last updated on 21/01/2021.


Course Name Code Semester Theory
Credit ECTS
SPECTRAL ESTIMATION ELE671 Any Semester/Year 3 0 3 8
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 will L.O.1. Recognizes spcetral estimation problems,
  2. L.O.2. Models the encountered problems in suitable forms,
  3. L.O.3. Knows which algorithms can be used to solve the problem established, knows advantages and disadvantages of these algorithms,
  4. L.O.4. Applies the techniques and algorithms learnt in the class in projects,
  5. 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-P. Stoica and R. Moses, Spectral Analysis of Signals, Pearson.
2-S. Kay, Modern Spectral Estimation, Prectice-Hall.
3-L. Marple, Digital Spectral Analysis, 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 6Sinusoid in White Noise.
Week 7Autocorrelation, Covariance, Modified Cov. Methods, Burg Algoritm.
Week 8Durbin?s Method(MA) , ARMA spectral Estimation.
Week 9Term Exam
Week 10Model Order Est., Minimum Variance Sp. Est., Filterbank.
Week 11Sinusoidal Parameter Est., Pisarenko, MUSIC, ESPRIT.
Week 12Higher Order Spcetrum (Bispectrum)
Week 13Nonstationary Spectral Estimation (Wigner, 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)14684
Presentation / Seminar Preparation000
Homework assignment8540
Midterms (Study duration)12525
Final Exam (Study duration) 13030
Total Workload3984236

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
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 effectively X   

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

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