NCHU Course Outline
Course Name (中) 偵測與預估(6862)
(Eng.) Detection and Estimation
Offering Dept Department of Electrical Engineering
Course Type Elective Credits 3 Teacher WEN,CHIH-YU
Department Industrial Technology Master Program in Electronic Systems(R)Graduate Language English Semester 2024-FALL
Course Description This course aims to present fundamental concepts in detection and estimation theory and its applications in signal processing and communications.
Prerequisites
self-directed learning in the course N
Relevance of Course Objectives and Core Learning Outcomes(%) Teaching and Assessment Methods for Course Objectives
Course Objectives Competency Indicators Ratio(%) Teaching Methods Assessment Methods
The intent is to provide the students sufficient knowledge to enable further contributions to be made thereby advancing this field of study.
topic Discussion/Production
Discussion
Lecturing
Exercises
Oral Presentation
Assignment
Quiz
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 Introduction
Week 2 Single Sample Detection of Binary Hypotheses
- Hypothesis Testing
- the MAP Criterion
Week 3 Bayes Criterion and Minimax Criterion
Neyman-Pearson Criterion and Sequential Detection
Week 4 Multiple Sample Detection of Binary Hypotheses
Week 5 The Optimum Digital Detector in Additive Gaussian Noise
Week 6 Performance of Binary Receivers in AWGN
Week 7 Detection of Signals with Random Parameters
- Composite Hypothesis Testing
- Unknown Phase/Amplitude/Frequency/Time of Arrival
Week 8 Detection of Multiple Hypotheses
- Bayes/MAP Criterion
- M-ary Decisions with and w/o Erasure
- Signal-Space Representations
- Performance of M-ary Detection Systems
- Sequential Detection of Multiple Hypotheses
Week 9 Mid-Term Exam
Week 10 Fundamentals of Estimation Theory
- Formulation of the General Parameter Estimation Problem
- Relationship between Detection and Estimation Theory
- Types of Estimation Problems
Week 11 Properties of Estimators
Bayes Estimation
Minimax Estimation
Maximum-Likelihood Estimation
Comparison of Estimators of Parameters
Week 12 Distribution-Free Estimation–Wiener Filters
- Orthogonality Principle
- Autoregressive Techniques
Week 13 - Discrete Wiener Filter
- Continuous Wiener Filter
- Generalization of Discrete and Continuous Filter Representations
Week 14 Distribution-Free Estimation–Kalman Filter
- Linear Least-Squares Methods
- Minimum-Variance Weighted Least-Squares Methods
- Minimum-Variance Least-Squares or Kalman Algorithm
Week 15 Kalman Algorithm Computational Considerations
Kalman Algorithm for Signal Estimation
Continuous Kalman Filter
Applications
Week 16 Final Project Presentation
Week 17 Final Project Presentation
Week 18 Final Project Presentation
Evaluation
Homework (30%), Midterm (40%), Presentation (30%)
Textbook & other References
Textbook: ”Detection and Estimation Theory” by Schonhoff and Giordano, Prentice Hall, 2006.
References:
1. Fundamentals of Statistical Signal Processing: Vol. 1 Estimation Theory; Vol. 2
Detection Theory by S.M. Kay
2. C.W. Helstrom, Probability and Stochastic Processes for Engineers
3. H.V. Poor, An Introduction to Signal Detection and Estimation
Teaching Aids & Teacher's Website
NCHU iLearning
Office Hours
TBD
Sustainable Development Goals, SDGs
09.Industry, Innovation and Infrastructureinclude experience courses:N
Please respect the intellectual property rights and use the materials legally.Please repsect gender equality.
Update Date, year/month/day:2024/08/27 14:47:10 Printed Date, year/month/day:2024 / 10 / 07
The second-hand book website:http://www.myub.com.tw/