| 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 |
self-directed
learning |
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(Link URL) |
| 09.Industry, Innovation and Infrastructure | include experience courses：N |
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