課程資訊
課程名稱
偵測與評估
DETECTION AND ESTIMATION THEORY 
開課學期
96-2 
授課對象
電機資訊學院  電信工程學研究所  
授課教師
李枝宏 
課號
EE5040 
課程識別碼
921 U1820 
班次
 
學分
全/半年
半年 
必/選修
選修 
上課時間
星期二6,7,8(13:20~16:20) 
上課地點
電二145 
備註
總人數上限:30人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/962921U1820_2008 
課程簡介影片
 
核心能力關聯
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課程大綱
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課程概述

CONTENTS:

I. Elements of Hypothesis Testing:
(a) Bayesian Detection Criterion.
(b) Minimum Probability of Error Detection Criterion.
(c) Minimax Detection Criterion.
(d) Maximum Likelihood (ML) Detection Criterion.
(e) Neyman-Pearson Detection Criterion.
(f) Composite Hypotheses Testing.


II. Signal Detection in Discrete Time:
(a) Models and Detector Structures.
(b) Performance Evaluation of Signal Detection Procedures.



III. Elements of Parameter Estimation:
(a) Estimation of Random Parameters − Bayesian Estimation Criterion, Maximum A Posteriori (MAP) Estimation Criterion.
(b) Estimation of Non-Random Parameters − Maximum Likehood (ML) Estimation Criterion.
(c) Multiple Parameter Estimation:
(1) Bayesian Estimation Criterion.
(2) MAP Estimation Criterion.
(3) ML Estimation Criterion.


IV. Representation of Random Signals:
(a) Orthogonal Representations for Deterministic Functions.
(b) Karhunen-Loeve Representation for Random Signals (KL Expansion).

IV. Detection of Signals in Continuous Time:
(a) The Detection of Signals in Aditive White Gaussian Noise (AWGN).
(b) The Detection of Signals in Aditive NonWhite Gaussian Noise.


V. Estimation of Signals in Continuous Time:
(a) The Estimation of Signals in Aditive White Gaussian Noise (AWGN).
(b) The Estimation of Signals in Aditive NonWhite Gaussian Noise.


VI. Optimum Linear Systems for Estimation:
(a) Innovation Sequences.
(b) Estimation, Prediction, and the Kalman Filter Theory.
 

課程目標
CONTENTS:

I. Elements of Hypothesis Testing:
(a) Bayesian Detection Criterion.
(b) Minimum Probability of Error Detection Criterion.
(c) Minimax Detection Criterion.
(d) Maximum Likelihood (ML) Detection Criterion.
(e) Neyman-Pearson Detection Criterion.
(f) Composite Hypotheses Testing.


II. Signal Detection in Discrete Time:
(a) Models and Detector Structures.
(b) Performance Evaluation of Signal Detection Procedures.



III. Elements of Parameter Estimation:
(a) Estimation of Random Parameters − Bayesian Estimation Criterion, Maximum A Posteriori (MAP) Estimation Criterion.
(b) Estimation of Non-Random Parameters − Maximum Likehood (ML) Estimation Criterion.
(c) Multiple Parameter Estimation:
(1) Bayesian Estimation Criterion.
(2) MAP Estimation Criterion.
(3) ML Estimation Criterion.


IV. Representation of Random Signals:
(a) Orthogonal Representations for Deterministic Functions.
(b) Karhunen-Loeve Representation for Random Signals (KL Expansion).

IV. Detection of Signals in Continuous Time:
(a) The Detection of Signals in Aditive White Gaussian Noise (AWGN).
(b) The Detection of Signals in Aditive NonWhite Gaussian Noise.


V. Estimation of Signals in Continuous Time:
(a) The Estimation of Signals in Aditive White Gaussian Noise (AWGN).
(b) The Estimation of Signals in Aditive NonWhite Gaussian Noise.


VI. Optimum Linear Systems for Estimation:
(a) Innovation Sequences.
(b) Estimation, Prediction, and the Kalman Filter Theory.
 
課程要求
PREREQUISITE COURSES:

Probability and Statistics, Signals and Systems, and Stochastic Signals and Systems are the requisitions for taking this course.
 
預期每週課後學習時數
 
Office Hours
每週一 09:10~10:00 備註: TA Office Hour: 地點:博理館527. 若欲找我討論功課, 請事先email告知(一天以前), 我人才會在實驗室. 
參考書目
(1) D. Kazakos and P. Papantoni-Kazakos, Detection and
Estimation, Computer Science Press, 1990.

(2) H. Vincent Poor, An Introduction to Signal Detection
and Estimation, Springer-Verlag, New York, 1988.

(3) H. Stark and J. W. Woods, Probability and Random
Processes with Applications to Signal Processing, 3rd
Edition
Prentice-Hall, 2002.
 
指定閱讀
 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
期中考 
45% 
 
2. 
期末考 
45% 
 
3. 
隨堂測驗 
0% 
 
4. 
作業 
10% 
 
5. 
報告 
0% 
 
 
課程進度
週次
日期
單元主題