Ders Bilgileri

#### Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
DETECTION AND ESTIMATION THEORY EEM 556 0 3 + 0 3 6
Ön Koşul Dersleri EEM 586 Probability and Random Variables
 Dersin Dili Türkçe Dersin Seviyesi Yüksek Lisans Dersin Türü SECMELI Dersin Koordinatörü Dr.Öğr.Üyesi GÖKÇEN ÇETİNEL Dersi Verenler Dersin Yardımcıları Arş. Gör. Burhan Baraklı Dersin Kategorisi Dersin Amacı 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 Dersin İçeriği 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
 Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri
 Öğretim Yöntemleri: Ölçme Yöntemleri:

#### Ders Akışı

Hafta Konular ÖnHazırlık
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

Ders Notu
Ders Kaynakları

#### Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
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
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
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
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
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
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
12 Ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment. X
13 Ability; to Access to wide and deep information with scientific researches in the field of Engineering, evaluate, interpret knowledge and implement. X
14 Ability; to Access to wide and deep information with scientific researches in the field of Engineering, evaluate, interpret knowledge and implement. X
15 Ability; To complete and implement “Limited or incomplete data” by using the scientific methods. To stick knowledge of different disciplinarians together. X
16 Ability; To complete and implement “Limited or incomplete data” by using the scientific methods. To stick knowledge of different disciplinarians together. X
17 Ability; to consolidate engineering problems, develop proper method to solve and apply innovative solutions. X
18 Ability; to consolidate engineering problems, develop proper method to solve and apply innovative solutions. X
19 Ability; To develop new and original ideas and methods, To develop new innovative solutions at design of system, component or process X
20 Ability; To develop new and original ideas and methods, To develop new innovative solutions at design of system, component or process X
21 Comprehensive information on modern techniques, methods and their borders which are being applied to engineering. X
22 Comprehensive information on modern techniques, methods and their borders which are being applied to engineering. X
23 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
24 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
25 High level ability to define the required information, data and reach, assess. X
26 High level ability to define the required information, data and reach, assess. X
27 Ability; To lead multi-disciplinary teams To take responsibility to define approaches for complex situations. X
28 Ability; To lead multi-disciplinary teams To take responsibility to define approaches for complex situations. X
29 Systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
30 Systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
31 Social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation, announcement. X
32 Social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation, announcement. X
33 Awareness at new and developing application of profession and ability to analyze and study on those applications. X
34 Awareness at new and developing application of profession and ability to analyze and study on those applications. X
35 Ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment. X
36 Ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment. X

#### Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 30
Odev 1 2
SozluSinav 1 25
PerformansGoreviSeminer 1 20
Odev 2 2
Odev 3 2
Odev 4 2
Odev 5 2
Odev 6 2
Odev 7 2
Odev 8 2
Odev 9 2
Odev 10 7
Toplam 100
Yıliçinin Başarıya Oranı 60
Finalin Başarıya Oranı 40
Toplam 100

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