DB004216-检测与估计（全英文）

研究生课程开设申请表

 开课院（系、所）：       

 课程申请开设类型： 新开     重开□     更名□（请在□内打勾，下同）

一、课程介绍（含教学目标、教学要求等）（300字以内）

本课程主要介绍基础的检测与估计理论。要求学生掌握假设检验的基本判决准则和基本的参数估计方法。假设检验的基本判决准则包括贝叶斯准则、Neyman-Pearson检验、复合假设检验等；参数估计方法包括贝叶斯参数估计、线性最小均方估计、线性最小方差无偏估计、最大后验概率估计、Cramer-Rao界及非线性估计等。本课程重点介绍的不是检测与估计的应用，而是各应用背后的统一理论。希望学生通过本课程的学习，可以将此通用的理论用于统计信号处理、通信或者控制方向的各种应用当中。

二、教学大纲（含章节目录）：（可附页）

1. 概率回顾：概率符号、随机矢量、方差矩阵、抽象矢量空间

2. 贝叶斯假设检验；似然比检验

3. Neyman-Pearson检验； ROC曲线

4. 复合假设检验；一致最大功效检验

5. 随机检验

6. M-ary假设检验

7. 性能分析与性能上下界

8. 贝叶斯参数估计

9. 线性最小二乘估计；正交性；正规方程

10. 非随机参数估计

11. 充分性、极小性、完整性

12. 最小方差无偏估计

13. Cramer-Rao界；有效性

14. 最大后验概率估计；性质

15. 非线性估计

三、教学周历

 注：1.以上一、二、三项内容将作为中文教学大纲，在研究生院中文网页上公布，四、五内容将保存在研究生院。2.开课学期为：春季、秋季或春秋季。3.授课语言为：汉语、英语或双语教学。4.适用学科范围为：公共，一级，二级，三级。5.实践环节为：实验、调研、研究报告等。6.教学方式为：讲课、讨论、实验等。7.学位课程考试必须是笔试。8.课件地址指在网络上已经有的课程课件地址。9.主讲教师简介主要为基本信息（出生年月、性别、学历学位、专业职称等）、研究方向、教学与科研成果，以100至500字为宜。

四、主讲教师简介：

刘楠：女，1978年12月出生。博士，教授。2001年于北京邮电大学电子工程系获学士学位，2007年获得美国马里兰大学电机与计算机工程博士，2007-2008年在美国斯坦福大学电子工程系无线系统实验室做博士后，2009年起于东南大学信息科学与工程学院任教授。研究领域为网络信息论。发表论文近40篇，其中IEEE Transactions七篇。任IEEE Transaction on Information Theory, IEEE Transaction on Communications, IEEE Transaction on Signal Processing等国际著名期刊审稿人。

五、任课教师信息（包括主讲教师）：

六、课程开设审批意见

说明：1．研究生课程重开、更名申请也采用此表。表格下载：http:/seugs.seu.edu.cn/down/1.asp

2．此表一式三份，交研究生院、院（系）和自留各一份，同时提交电子文档交研究生院。

研究生院    2003.12

Application Form For Opening Graduate Courses

School (Department/Institute)：

Course Type: New Open    Reopen □   Rename □（Please tick in □, the same below）

1. Course Introduction (including teaching goals and requirements) within 300 words:

The class introduces the basic theory of detection and estimation. Students are required to learn the basic methods for detection and estimation of parameters.Basic methods for detection include Bayesian hypothesis testing, Neyman-Pearson tests; composite tests etc. Methods of parameter estimation include Bayesian parameter estimation, linear LS estimation, minimum variance unbiased estimation, maximum likelihood estimation, Cramer-Rao bound and nonlinear estimation. The focus of this course is not on the applications of estimation and detection, but rather the common problem solving framework that they all share. It is hoped that after this class, students can use the theory learnt and apply them successfully to all kinds of applications in the area of statistical signal processing, communications and control.

2. Teaching Syllabus (including the content of chapters and sections. A sheet can be attached):

1. Review of probability: probability notation, random vectors, covariance matrices, abstract vector space

2. Bayesian hypothesis testing; likelihood ration tests;

3. Neyman-Pearson tests; receiver operating characteristics

4. Composite hypothesis testing; uniformly most powerful tests

5. Randomized tests

6. M-ary hypothesis testing

7. Performance evaluation; bounds

8. Bayesian parameter estimation

9. Linear least-squares estimation; orthogonality; normal equations

10. Nonrandom parameter estimation

11. Sufficiency, minimality, completeness

12. Minimum variance unbiased estimation

13. Cramer-Rao bound; efficiency

14. Maximum likelihood estimation; properties

15. Nonlinear estimation

3. Teaching Schedule:

Note: 1.Above one, two, and three items are used as teaching Syllabus in Chinese and announced on the Chinese website of Graduate School. The four and five items are preserved in Graduate School.

2. Course terms: Spring, Autumn , and Spring-Autumn term.  

3. The teaching languages for courses: Chinese, English or Chinese-English.

4. Applicable range of discipline: public, first-class discipline, second-class discipline, and third-class discipline.

5. Practice includes: experiment, investigation, research report, etc.

6. Teaching methods: lecture, seminar, practice, etc.

7. Examination for degree courses must be in paper.

8. Teaching material websites are those which have already been announced.

9. Brief introduction of chief lecturer should include: personal information (date of birth, gender, degree achieved, professional title), research direction, teaching and research achievements. (within 100-500 words)

4. Brief Introduction of Chief lecturer:

Nan Liu: female, born in December, 1978. Ph.D., professor in Southeast University. She received the B.Eng. degree in electrical engineering from Beijing University of Posts and Telecommunications, Beijing, P. R. China in 2001, and the Ph.D. degree in electrical and computer engineering from University of Maryland, College Park, MD, USA in 2007.  From 2007-2008, she was a postdoctoral scholar in the Wireless Systems Lab, Department of Electrical Engineering, Stanford University. In 2009, she became a faculty member in the School of Information Science and Engineering in Southeast University, Nanjing, China. Her research interests are in network information theory for wireless networks. She has published nearly 40 papers including 7 journal papers in the IEEE Transactions.

5. Lecturer Information (include chief lecturer)