Course Name Code Semester T+U Hours Credit ECTS
Detection and Estimation Theory EEM 556 0 3 + 0 3 6
Precondition Courses EEM 586 Probability and Random Variables
Recommended Optional Courses
Course Language Turkish
Course Level yuksek_lisans
Course Type Optional
Course Coordinator Doç.Dr. GÖKÇEN ÇETİNEL
Course Lecturers
Course Assistants Arş. Gör. Burhan Baraklı
Course Category
Course Objective Noise is not known a priori in most signal processing problems. For this reason, signal processing algorithms developed ignoring unknown noise will give erroneous results. The goal of this course is to determine the most suitable data generation model that satisfies a given noisy data by taking the noise into consideration and to discuss estimation of signal and noise parameters provided that they are unknown
Course Content Linear model, small and large sample properties of estimators, least squares (LS) estimation, maximum likelihood (ML) estimation, mean square (MSE) and maximum a posteriori probability (MAP) estimation of random parameters, basic hypothesis test, hypothesis test in case of unknowns, detection of signals in Gaussian noise, detection in case of uncertainties
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Learning the least squares estimation algorithm
2 Discussing maximum likelihood estimation
3 Investigating mean square estimation and maximum a posteriori estimation
4 Comprehending hypothesis test problem for two or more hypotheses
5 Performing hypothesis test in case of unknown parameters
Week Course Topics Preliminary Preparation
1 Discussion of linear model, examples involving linear model, and notation formation
2 Exploration of small and large sample properties of estimators such as unbiasedness, efficiency, consistency, asymptotic ubiasedness and efficiency
3 Introducing LS estimation, derivation of estimation formula, discussion of small and large sample properties of LS estimators
4 Computation of LS estimators by using singular point expansion, recursive LS estimators
5 Principles of ML estimation, derivation of estimation formula, various applications
6 Exploration of small and large sample properties of ML estimators
7 MSE and MAP estimation computation of random variables
8 Introduction to detection theory
9 Likelihood ratio test, measures in hypothesis test, model consistency test
10 Detection in case of more than two hypotheses, evaluation of detection performance
11 Investigating detection in case of unknown deterministic and random parameters
12 Detection of signals in white and colored Gaussian noise, validity of Gaussian noise assumption
13 Detection examples in case of uncertainties
14 Detailed comparison of the detection algorithms discussed
Resources
Course Notes
Course Resources
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 Ability; to Access to wide and deep information with scientific researches in the field of Engineering, evaluate, interpret knowledge and implement. X
1 Ability; to Access to wide and deep information with scientific researches in the field of Engineering, evaluate, interpret knowledge and implement. X
1 Ability; to Access to wide and deep information with scientific researches in the field of Engineering, evaluate, interpret knowledge and implement. X
2 Ability; To complete and implement “Limited or incomplete data” by using the scientific methods. To stick knowledge of different disciplinarians together. X
2 Ability; To complete and implement “Limited or incomplete data” by using the scientific methods. To stick knowledge of different disciplinarians together. X
2 Ability; To complete and implement “Limited or incomplete data” by using the scientific methods. To stick knowledge of different disciplinarians together. X
3 Ability; to consolidate engineering problems, develop proper method to solve and apply innovative solutions. X
3 Ability; to consolidate engineering problems, develop proper method to solve and apply innovative solutions. X
3 Ability; to consolidate engineering problems, develop proper method to solve and apply innovative solutions. X
4 Ability; To develop new and original ideas and methods, To develop new innovative solutions at design of system, component or process X
4 Ability; To develop new and original ideas and methods, To develop new innovative solutions at design of system, component or process X
4 Ability; To develop new and original ideas and methods, To develop new innovative solutions at design of system, component or process X
5 Comprehensive information on modern techniques, methods and their borders which are being applied to engineering. X
5 Comprehensive information on modern techniques, methods and their borders which are being applied to engineering. X
5 Comprehensive information on modern techniques, methods and their borders which are being applied to engineering. X
6 Ability; to design and apply analytical, modeling and experimental based research, analyze and interpret the faced complex issues during the design and apply process. X
6 Ability; to design and apply analytical, modeling and experimental based research, analyze and interpret the faced complex issues during the design and apply process. X
6 Ability; to design and apply analytical, modeling and experimental based research, analyze and interpret the faced complex issues during the design and apply process. X
7 High level ability to define the required information, data and reach, assess. X
7 High level ability to define the required information, data and reach, assess. X
7 High level ability to define the required information, data and reach, assess. X
8 Ability; To lead multi-disciplinary teams To take responsibility to define approaches for complex situations. X
8 Ability; To lead multi-disciplinary teams To take responsibility to define approaches for complex situations. X
8 Ability; To lead multi-disciplinary teams To take responsibility to define approaches for complex situations. X
9 Systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
9 Systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
9 Systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
10 Social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation, announcement. X
10 Social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation, announcement. X
10 Social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation, announcement. X
11 Awareness at new and developing application of profession and ability to analyze and study on those applications. X
11 Awareness at new and developing application of profession and ability to analyze and study on those applications. X
11 Awareness at new and developing application of profession and ability to analyze and study on those applications. X
12 Ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment. X
12 Ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment. X
12 Ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment. X
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 30
1. Ödev 2
1. Sözlü Sınav 25
1. Performans Görevi (Seminer) 20
2. Ödev 2
3. Ödev 2
4. Ödev 2
5. Ödev 2
6. Ödev 2
7. Ödev 2
8. Ödev 2
9. Ödev 2
10. Ödev 7
Total 100
1. Yıl İçinin Başarıya 60
1. Final 40
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 2 32
Mid-terms 10 5 50
Quiz 1 20 20
Assignment 1 10 10
Oral Examination 1 10 10
Performance Task (Laboratory) 1 20 20
Total Workload 190
Total Workload / 25 (Hours) 7.6
dersAKTSKredisi 6