| 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 |