MS004122-估计理论

研究生课程开设申请表

开课院（系、所）：       金沙js800000

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）

本课程是一门针对信息工程类硕士研究生的专业限选英文课程，它向学生介绍如何用数理统计的手段解决实际工程中遇到的问题，介绍一些在干扰背景下经典统计参数估计原理、方法及应用。

二、教学大纲（含章节目录）：（可附页）

第一章：绪论（2学时）

统计信号处理研究背景与现状

第二章：最小方差无偏估计（2学时）

最小方差无偏估计的统计概念及分析方法

第三章：Cramer-Rao下限（2学时）

Cramer-Rao下限的物理意义及计算

第四章：线性模型（2学时）

线性模型的定义和性质

第五章：一般最小方差无偏估计（2学时）

一般最小方差无偏估计的定义和性质

第六章：最佳线性无偏估计器（2学时）

最佳线性无偏估计器的定义和性质

第七章：最大似然估计（2学时）

最大似然估计的定义和性质

第八章：最小二乘估计（2学时）

最小二乘估计方法及其几何解释、约束最小二乘估计、非线性最小二乘法

第九章：矩方法（2学时）

矩的定义及应用

第十章：贝叶斯原理（2学时）

贝叶斯原理的统计概念及方法

第十一章：一般贝叶斯估计量（2学时）

一般贝叶斯估计量的定义及计算方法

第十二章：线性贝叶斯估计量（2学时）

线性贝叶斯估计量的定义及计算方法

第十三章：卡尔曼估计（2学时）

卡尔曼估计原理及应用

第十四章：估计量总结（2学时）

估计量的总结

第十五章：复数据和复参数的扩展（2学时）

各类估计方法在复数域的扩展

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：

夏亦犁，1984年7月生，教授，博士生导师。2006年获金沙js800000信息工程专业学士学位；2010年获英国帝国理工学院信号处理专业博士学位；2011年2012年于帝国理工学院通信与信号处理实验室从事博士后研究；2013年起任职于金沙js800000信号处理学科；2014年入选江苏省双创人才计划，2018年入选金沙js800000至善青年学者。已主持和承担国家自然科学基金、省部级科研项目多项；已发表和录用SCI/EI学术论文80余篇；已获授权国际和国家发明专利十余项。

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。

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:

本课程是一门针对信息工程类硕士研究生的专业限选英文课程，它向学生介绍如何用数理统计的手段解决实际工程中遇到的问题，介绍一些在干扰背景下经典统计参数估计原理、方法及应用。

This English course is specifically designed for postgraduates in information engineering. It introduces solutions to statistical problems in practice, including principles and applications of classic parameter estimation methods.

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

 Chapter 1: Introduction (2 hours)

 Background of Statistical Signal Processing

 Chapter 2: Minimum Variance Unbiased Estimation (2 hours)

 Concepts and Analysis of Minimum Variance Unbiased Estimation

 Chapter 3: Cramer-Rao Lower Bound (2 hours)

 Physical meaning of Cramer-Rao Lower Bound and its calculation

 Chapter 4: Linear Model (2 hours)

 Definition of linear model and its properties

 Chapter 5: General Minimum Variance Unbiased Estimation (2 hours)

 Definition of General Minimum Variance Unbiased Estimation and its properties

 Chapter 6: Best Linear Unbiased Estimators (2 hours)

 Definition of Best Linear Unbiased Estimators and their properties

 Chapter 7: Maximum Likelihood Estimation (2 hours)

 Definition of Maximum Likelihood Estimation and its properties

 Chapter 8: Least Squares Estimation (2 hours)

 Least squares estimator and its geometric explanation, nonlinear least squares  

 Chapter 9: Method of Moments (2 hours)

 Definition of moments and their applications

 Chapter 10: Bayesian Philosophy (2 hours)

 Concepts of Bayesian Philosophy and its methods

 Chapter 11: General Bayesian Estimators (2 hours)

    Concepts of General Bayesian Estimators, risk function

 Chapter 12：Linear Bayesian Estimators (2 hours)

 Linear Bayesian Estimators, least minimum mean square error

 Chapter 13: Kalman Filters

 Principles of Kalman Filters and their applications

 Chapter 14: Summary of Estimators（2 hours）

    Summary of estimators

 Chapter 15: Extensions for Complex Data and Parameters

 Estimators in the complex domain

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:

 Yili Xia received the B.Eng. degree in information engineering from Southeast University, Nanjing, China, in 2006, the M.Sc. degree (Hons.) in communications and signal processing from the Department of Electrical and Electronic Engineering, Imperial College London, London, U.K., in 2007, and the Ph.D. degree in adaptive signal processing from Imperial College London, in 2011. Since 2013, he has been an Associate Professor in signal processing with the School of Information Science and Engineering, Southeast University, Nanjing, China, where he is currently a Professor. His research interests include complex and hyper-complex statistical analysis, detection and estimation, linear and nonlinear adaptive filters, and their applications on communications, power systems, and images.

5. Lecturer Information (include chief lecturer)