MS004122-估計理論

研究生課程開設申請表

開課院（系、所）：       beat365正版唯一

課程申請開設類型： 新開√     重開□     更名□（請在□内打勾，下同）

一、課程介紹（含教學目标、教學要求等）（300字以内）

本課程是一門針對信息工程類碩士研究生的專業限選英文課程，它向學生介紹如何用數理統計的手段解決實際工程中遇到的問題，介紹一些在幹擾背景下經典統計參數估計原理、方法及應用。

二、教學大綱（含章節目錄）：（可附頁）

第一章：緒論（2學時）

統計信号處理研究背景與現狀

第二章：最小方差無偏估計（2學時）

最小方差無偏估計的統計概念及分析方法

第三章：Cramer-Rao下限（2學時）

Cramer-Rao下限的物理意義及計算

第四章：線性模型（2學時）

線性模型的定義和性質

第五章：一般最小方差無偏估計（2學時）

一般最小方差無偏估計的定義和性質

第六章：最佳線性無偏估計器（2學時）

最佳線性無偏估計器的定義和性質

第七章：最大似然估計（2學時）

最大似然估計的定義和性質

第八章：最小二乘估計（2學時）

最小二乘估計方法及其幾何解釋、約束最小二乘估計、非線性最小二乘法

第九章：矩方法（2學時）

矩的定義及應用

第十章：貝葉斯原理（2學時）

貝葉斯原理的統計概念及方法

第十一章：一般貝葉斯估計量（2學時）

一般貝葉斯估計量的定義及計算方法

第十二章：線性貝葉斯估計量（2學時）

線性貝葉斯估計量的定義及計算方法

第十三章：卡爾曼估計（2學時）

卡爾曼估計原理及應用

第十四章：估計量總結（2學時）

估計量的總結

第十五章：複數據和複參數的擴展（2學時）

各類估計方法在複數域的擴展

三、教學周曆

注：1.以上一、二、三項内容将作為中文教學大綱，在研究生院中文網頁上公布，四、五内容将保存在研究生院。2.開課學期為：春季、秋季或春秋季。3.授課語言為：漢語、英語或雙語教學。4.适用學科範圍為：公共，一級，二級，三級。5.實踐環節為：實驗、調研、研究報告等。6.教學方式為：講課、讨論、實驗等。7.學位課程考試必須是筆試。8.課件地址指在網絡上已經有的課程課件地址。9.主講教師簡介主要為基本信息（出生年月、性别、學曆學位、專業職稱等）、研究方向、教學與科研成果，以100至500字為宜。

四、主講教師簡介：

夏亦犁，1984年7月生，教授，博士生導師。2006年獲beat365正版唯一信息工程專業學士學位；2010年獲英國帝國理工學院信号處理專業博士學位；2011年2012年于帝國理工學院通信與信号處理實驗室從事博士後研究；2013年起任職于beat365正版唯一信号處理學科；2014年入選江蘇省雙創人才計劃，2018年入選beat365正版唯一至善青年學者。已主持和承擔國家自然科學基金、省部級科研項目多項；已發表和錄用SCI/EI學術論文80餘篇；已獲授權國際和國家發明專利十餘項。

五、任課教師信息（包括主講教師）：

六、課程開設審批意見

說明：1．研究生課程重開、更名申請也采用此表。表格下載：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，同時提交電子文檔交研究生院。

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:

本課程是一門針對信息工程類碩士研究生的專業限選英文課程，它向學生介紹如何用數理統計的手段解決實際工程中遇到的問題，介紹一些在幹擾背景下經典統計參數估計原理、方法及應用。

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)