S004113-4107-统计信号处理

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研究生课程开设申请表

 开课院(系、所):信息科学与工程学院       

 课程申请开设类型: 新开□     重开     更名□请在内打勾,下同

课程

名称

中文

统计信号处理

英文

Statistical Signal Processing

待分配课程编号

S004107

课程适用学位级别

博士


硕士

总学时

40

课内学时

32

学分

2

实践环节

课程设计

用机小时

16

课程类别

公共基础     专业基础     专业必修     专业选修

开课院()

信息科学与工程学院

开课学期

春季

考核方式

A.笔试(开卷   闭卷)      B. 口试    

C.笔试与口试结合                 D. □其他

课程负责人

教师

姓名

方世良

职称

教授

e-mail

slfang@seu.edu.cn

网页地址


授课语言


课件地址


适用学科范围

一级

所属一级学科名称

信息与通信工程

实验(案例)个数

2

先修课程

随机过程、信号与系统、数字信号处理

教学用书

教材名称

教材编者

出版社

出版年月

版次

主要教材

统计信号处理基础

方世良




主要参考书

随机信号处理

陈炳和

国防工业出版社

1996


An Introduction to statistical signal processing with Applications

M.D.Srinath

&P.K.Rajasekaran




Detection,Estimation,and Modulation Theory

Harry L.Van Trees





一、课程介绍(含教学目标、教学要求等)300字以内)

建立从统计观点出发的信号处理基本观念,掌握信号处理的基本环节:检测、估计、统计识别、多元阵列统计信号处理的主要概念和方法。了解信号处理系统的总体设计思路和结构。为研究生进一步学习和研究信号处理奠定基础。

二、教学大纲(含章节目录):(可附页)

1  绪论

2  统计信号处理基础

3  检测理论

  1. bayes准则

  2. 其它准则

  3. 接收机工作特性

  4. 多元假设检验

  5. 复合假设检验

  6. 序列检验

4背景噪声中信号的检测

  1. 确知信号检测

  2. 随机参量信号检测

  3. 高斯信号检测

  4. 最佳线性滤波器

5信号参量的估计

  1. 概述

  2. 估计量的性质

  3. 随机参量的估计

  4. 最大似然估计

  5. 线性最小均方估计

  6. 最小二乘估计

6波形估计

  1. 概述

  2. 维纳滤波

  3. 卡尔曼滤波

  4. 卡尔曼滤波的推广

  5. 最小二乘估计

7Robust检测和Robust估计初探

  1. Robust检测

  2. Robust估计

8信号频谱分析

9多元阵列信号处理

10统计信号分类识别

11  模糊函数

三、教学周历

 周次

 教学内容

 教学方式

1

绪论,统计信号处理基础,bayes准则

讲课

2

检测理论:其它准则,接收机工作特性,多元假设检验,复合假设检验,序列检验

讲课

3

背景噪声中信号的检测:确知信号检测,随机参量信号检测,高斯信号检测

讲课

4

背景噪声中信号的检测:最佳线性滤波器

讲课

5

课程设计一:非白噪声中信号的检测

上机

6

信号参量的估计

讲课

7

波形估计:概述,维纳滤波,卡尔曼滤波及推广,最小二乘估计

讲课

8

课程设计二:卡尔曼滤波

上机

9

Robust检测和Robust估计初探

讲课

10

信号频谱分析

讲课

11

阵列信号处理

讲课

12

统计信号分类识别

讲课

13

模糊函数

讲课

14

考试


15



四、主讲教师简介:

方世良,男,19598月出生,教授,博士生导师。研究方向为信号与信息处理。主要从事信号处理、水声电子工程等领域的研究工作,对信号检测、估计和目标分类识别、阵列信号处理及软硬件系统开发等有较深入的研究。先后负责和参加十多项型号、设备、预研等重点科研项目的研究,获国家科技进步二、三等奖各一项,部级科技进步一、三等奖各二项,二等奖四项。现为中国声学学会青年工作委员会委员、中国声学学会理事、水声学会委员、江苏省声学学会副理事长。

五、任课教师信息(包括主讲教师):

 任课

 教师

 学科

 (专业)

 办公

 电话

 住宅

 电话

 手机

 电子邮件

 通讯地址

 邮政

 编码

 方世良

 信号与信息处理

83795171



slfang@seu.edu.cn

 东南大学信息科学与工程学院

210096





Application Form For Opening Graduate Courses

School (Department/Institute)School of Information Science and Engineering

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

Course Name

Chinese

统计信号处理

English

Statistical Signal Processing

Course Number

S004107

Type of Degree

Ph. D


Master

Total Credit Hours

40

In Class Credit Hours

32

Credit

2

Practice

Experiments

Computer-using Hours

16

Course Type

Public Fundamental    □Major Fundamental    □Major Compulsory     □Major Elective

School (Department)

School of Information Science and Engineering

Term

springtime

Examination

A.Paper √Open-book   □ Closed-bookB. □Oral   

C. □Paper-oral Combination                       D. □ Others

Chief

Lecturer

Name

Fang Shiliang

Professional Title

Professer

E-mail

slfang@seu.edu.cn

Website


Teaching Language used in Course

Chinense

Teaching Material Website


Applicable Range of Discipline

One class

Name of First-Class Discipline

Information and communication Engieering

Number of Experiment

2

Preliminary Courses

Probability and statistical process

Teaching Books

Textbook Title

Author

Publisher

Year of Publication

Edition Number

Main Textbook






Main Reference Books

Random signal processing

Chen Binhe

Publishing House of national defence Industry

1996


An Introduction to statistical with Applications

M.D.Srinath

&P.K.Rajasekaran




Detection,Estimation, and Modulation Theory

Harry L.Van Trees

Jone Wiley &Sons,Inc

2001



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

In this course, the detection theory and the estimation theory are studied and some basic concepts about signal processing are established with statistical theory. The goal of this course is to develop these theories in a common mathematical framework and demonstrate how they can be used to solve wealth of practical problems in many diverse physical situations.


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

1  Introduction

2  Bases of Statistical Signal Processing

3  The Detection Theory

  1. Bayes Criterion

  2. Other Criterion

  3. Receive Operation Characteristic

  4. M Hypotheses Tests

  5. Composite Hypotheses Tests

  6. Sequence Tests

4Detection of Signals in Noise Background

  1. Detection of Known Signals

  2. Detection of Random Parameters Signals

  3. Detection of Gaossian Signals

  4. Optimum Linear Filters

5Estimation of Signal Parameters

  1. Introduction

  2. Properties of Estimator

  3. Estimation of Random Parameters

  4. Maximum Likelihood Estimation

  5. Linear Minimum Mean Square Estimation

  6. Minimum Square Estimation

6Estimation of Waveforms

  1. Introduction

  2. Wiener Filters

  3. Kalman Filters

  4. Generalization of Kalman Filters

  5. Minimum Square Estimation of Waveforms

7Robust Detection and Robust Estimation

  1. Robust Detection

  2. Robust Estimation

8Spectrum Analyse

9Array Signal Processing

10Statistical Signal Recognition

11  Fuzzy Function


  1. Teaching Schedule:


Week

Course Content

Teaching Method

1

Introduction,Bases of Statistical Signal Processing, Bayes Criterion

Lecturing

2

Other Criterion, Receive Operation Characteristic M Hypotheses Tests, Composite Hypotheses Tests, Sequence Tests

Lecturing

3

Detection of Known Signals, Detection of Random Parameters Signals, Detection of Gaossian Signals

Lecturing

4

Optimum Linear Filters

Lecturing

5

Detection of Random Signals in Nonwhite Gaossian Noise

Experiment

6

Estimation of Signal Parameters

Lecturing

7

Wiener Filters, Kalman Filters,Minimum Square Estimation of Waveforms

Lecturing

8

Kalman Filters

Experiment

9

Robust Detection and Robust Estimation

Lecturing

10

Spectrum Analyse

Lecturing

11

Array Signal Processing

Lecturing

12

Statistical Signal Recognition

Lecturing

13

Fuzzy Function

Lecturing

14

examination


15



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)


  1. Brief Introduction of Chief lecturer:

Name: Fang Shiliang

Sex: male

Birth date: August , 1960

Professional Title: Professor

Research specialities are underwater acoustic engineering, signal processingand its application. Research interests include acoustic signal detection, signal parameter estimation, target recognitionand so on. Many science researchprogramswere well achieved. Many science and technology progress awards were won.




  1. Lecturer Information (include chief lecturer)


Lecturer

Discipline

(major)

Office

Phone Number

Home Phone Number

Mobile Phone Number

Email

Address

Postcode

Fang Shiliang

Signal Processing

83795171



slfang@seu.edu.cn


210096







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