檢測與估計理論

We introduce various estimators and the corresponding estimation theory. We cover the topics including: minimum variance unbiased (MVU) estimation, Cramer-Rao Lower Bound (CRLB), linear model, sufficient statistics, best linear unbiased estimators, maximum likelihood estimation, least square approach, Bayesian estimators, hypothesis testing, various detectors for deterministic and random signals. Note that this time Detection Theory is covered.

Random process, probability and linear algebra

Lecture notes will be made available on the e3 website Office hour Wednesday 15:30-16:30, or by appointment, at EE815. TAs 廖昱傑 ycliao1203.ee10@nycu.edu.tw 謝旻辰 tim0408.ee10@nycu.edu.tw 鄭秉承 pccheng.ee10@nycu.edu.tw 劉志奇 damon91117.ee10@nycu.edu.tw 游佳欣 jihsyu.ee10@nycu.edu.tw

Homework: 20% (Some homework requires the use of Matlab) Midterm-I: 25% Midterm-II: 25% Final: 30%

• 1. Minimum variance unbiased estimation 2. Cramer-Rao lower bound 3. Linear models 4. General minimum variance unbiased estimation 5. BLUE 6. ML estimation 7. LS estimation 8. Bayesian estimations 9. Neyman-Pearson Theorem 10. Detection for deterministic signals

Text Book [1] Steven M. Kay, “Fundamentals of Statistical Signal Processing: Volume I Estimation Theory,” Prentice Hall 1993. [2] Steven M. Kay, “Fundamentals of Statistical Signal Processing: Volume II Detection Theory,” Prentice Hall 1998. Reference Books [1] H. Stark and J. W. Woods, Probability and Random Processes with Applications to Signal Processing, Prentice Hall, 2001 [2] A. Papoulis and S. U. Pillai, Probability, Random Variables and Stochastic Process, McGraw-Hill, New York US, 2002 [3] Gilbert Strang, Linear Algebra and Its Applications, Brooks Cole, 2005

地點

EE722

時間

16:40-17:30, Thursday, or by appointment

聯絡方式

Ext. 54346 Email: shanghot@mail.nctu.edu.tw