Detection and Estimation
| Title | Θεωρία Εκτίμησης και Ανίχνευσης / Detection and Estimation |
| Code | 080 |
| Faculty | Engineering |
| School | Electrical and Computer Engineering |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Panagiotis Petrantonakis |
| Common | No |
| Status | Active |
| Course ID | 600001033 |
Programme of Study: Electrical and Computer Engineering
Registered students: 3
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| ELECTRICAL ENERGY | Elective Courses | 8 | 4 | 5 |
| ELECTRONICS AND COMPUTER ENGINEERING | Elective Courses | 8 | 4 | 5 |
| TELECOMMUNICATIONS | Elective Courses | 8 | 4 | 5 |
| Academic Year | 2021 – 2022 |
| Class Period | Spring |
| Faculty Instructors | |
| Weekly Hours | 4 |
| Class ID | 600205481
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600205481
- At the Website of the School: http://alexander.ee.auth.gr:8083/eTHMMY/
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Prerequisites
General Prerequisites
1. Theory of probability and statistics
2. Linear algebra
3. Signals and systems
4. Stochastic signals
5. Digital signal processing
Learning Outcomes
The course aims to expose students and help them comprehend:
1. The problem of estimation and its applications
2. The various estimator structures and their properties
3. The problem of detection and its applications
4. The hypothesis tasting (formulation and solution)
5. The various methods of deterministic and stochastic signal detection
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Generate new research ideas
- Advance free, creative and causative thinking
Course Content (Syllabus)
Estimation theory:
General Minimum Variance Unbiased Estimation
Cramer-Rao lower bound
Minimum Variance Unbiased Estimation of linear model parameters
Best linear unbiased estimator
Maximum likelihood estimators
Least squares estimators
Bayes estimator
Wiener filter
Kalman filter
Theory of detection:
Theory of statistical decisions.
Hypothesis testing and the Neyman – Pearson theorem
Hypothesis testing via Bayes risk minimization
ROCs
Detection of deterministic signals in noise, the matched filter.
Detection of random signals in noise
Educational Material Types
- Notes
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 57 | 1.9 | |||
| Tutorial | 55 | 1.8 | |||
| Written assigments | 6 | 0.2 | |||
| Exams | 32 | 1.1 | |||
| Total | 150 | 5 |
Student Assessment
Description
Written examination
Optional homework assignments
Student Assessment methods
- Written Exam with Extended Answer Questions (Summative)
- Written Exam with Problem Solving (Summative)
Bibliography
Course Bibliography (Eudoxus)
1α. Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory, by Steven M. Kay, Prentice Hall, 1993
1β. Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, by Steven M. Kay, Prentice Hall 1998.
2. Σημειώσεις
Last Update
17-12-2015
