DB004103-现代信号分析与处理技术

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）

一、课程介绍（含教学目标、教学要求等）（300字以内）

本课程教学内容主要取材于信号与信息处理、现代数字通信的基础理论与前沿技术，目的在于使学生较全面地掌握现代信号分析与处理的理论基础和先进的分析方法与设计技术，并且通过跟踪本学科的最新发展趋势和热门研究课题，培养学生具备适应未来新的交叉学科发展的综合创新能力，并能灵活应用所学的知识解决相关的实际工程问题，在信息与通信工程领域培养一定的独立科研工作能力。

本课程采取教师讲课、学生专题研究实践和作报告相结合的方式组织教学。

二、教学大纲（含章节目录）（可附页）

第一章参数估计方法

1.1 参数估计的基本性能

1.2 随机信号统计量的样本估计

1.3 最小二乘估计(LS)

1.4 线性均方估计(LMMSE)

1.5 最大似然估计(ML)与EM算法

1.6  Bayes估计

第二章最优滤波方法

2.1 滤波、预测、反卷积与噪声抑制

2.2 FIR维纳滤波器

2.3 IIR维纳滤波器

2.4 卡尔曼滤波器－最优线性序贯贝叶斯滤波

2.5 粒子滤波器－序贯MC贝叶斯滤波

第三章自适应滤波

3.1 LMS类自适应滤波器

3.2基本RLS自适应滤波算法及快速实现

3.3基于QR分解的RLS滤波器

3.4非线性自适应滤波

3.5自适应滤波的应用

第四章阵列信号处理

4.1引言

4.2基本的波束形成方法

1) min MSE;                    2) max SINR;

3) LCMV(线性约束最小方差法);   4) 广义旁瓣对消器(零点形成技术)

4.3自适应波束形成

1) 采样矩阵求逆(SMI,或称DMI,直接求逆)和自适应滤波法;

2)投影变换法;        3)基于特征空间的自适应波束形成

4.4 波达方向(DOA)估计

1) 经典空间谱估计法;        2) MVDR法;      3) 最大似然(ML)法

4) MUSIC类算法;            5) ESPRIT类算法

第五章通信中的信号处理

5.1通信中的信道均衡

1) 线性均衡器(ZF, MMSE);     2)非线性判决反馈均衡器(DFE);

3) 频域均衡器(FDE);          4) 分数抽头均衡器(FSE):线性;DFE-FSE;FD-FSE

5) 最大似然序列估计(MLSE)均衡器

5.2通信中的信道估计

1) 基于训练序列的LS信道估计;     2) 基于叠加训练序列的信道估计;

3) OFDM通信系统中的信道估计

5.3 MIMO通信中的空时信号处理

1) MIMO空时处理概述;       

2) 无CSIT: 空时发射分集技术,如空时码、延时分集等;

3) 无CSIT: 空间复用技术, 如BLAST等;

4) 有CSIT: 闭环MIMO技术, 如发射波束形成、预编码等;

5) 基于有限反馈的预编码

5.4多用户通信系统中的信号处理

1) OFDMA、空分多址(SDMA)和其他多址技术;

2) 多用户预编码技术;               3) 随机多波束形成与多用户分集;

5.5 协作通信系统中的信号处理

第六章  多速率数字信号处理与滤波器组

6.1 数字信号的采样率变换

1) M倍降采样;

2) L倍升采样;

3) 分数倍采样率变换

6.2 多速率处理模块的级联等效形式

6.3 抽取器和插值器的多级实现

6.4 多相分解结构

1) 基于多相分解的FIR滤波器实现结构;

2) 升采样器和降采样器的高效实现结构

6.5 数字滤波器组：

1) 简单的最大均匀抽取DFT滤波器组;

2) 多子带滤波器;

3) 两通道滤波器组及其优化设计;

4) 多通道滤波器组(余弦调制滤波器组)

第七章信号的时频分析

7.1连续时间短时傅里叶变换

7.2离散信号的短时傅里叶变换

7.3 Wigner-Ville分布及其变型

7.4模糊度函数和Cohen类时频分布

7.5时-频分布的应用实例

第八章盲信号处理与盲源分离

8.1 基于信息论的盲源分离方法

8.2 基于高阶统计量逼近的盲源分离算法

8.3 盲信号抽取与ICA分析

8.4 盲信号分离的欠定问题

8.5 非线性混叠信号的盲分离

第九章信道盲估计与盲均衡

9.1 信道估计与信道均衡

9.2 基于高阶统计量和二阶循环平稳特性的信道盲均衡

9.3 基于子空间分析的信道盲估计与盲均衡

9.4 基于线性预测模型的信道盲估计与盲均衡

9.5 MIMO信道的盲估计与盲均衡

三、教学周历

（第二章、第七章由王桥主讲，其余由杨绿溪主讲）

四、主讲教师简介：

杨绿溪：1964年生，东南大学信息科学与工程学院教授，博士生导师，1993年获博士学位。近年来主要从事MIMO通信系统设计、协作通信与分集处理、多用户MIMO方案、有限反馈预编码等方面的科研工作，已申请发明专利20项，提交3GPP2 UMB国际通信标准提案3份，并在包括IEEE信号处理、通信、电路与系统会刊和中国科学E辑、F辑等国内外刊物与IEEE会议上发表和合作发表论文200多篇，SCI收录30多篇，EI收录120多篇。曾担任国家863项目负责人、国家攀登计划重大项目子课题组长、国家自然科学基金重点项目课题组长等，参加过国家自然科学基金重大项目的研究；主持过4项国家自然科学基金，和其它10多项省部级科研项目，结题评审均为“优”，其中2项被评为“特优”。曾作为主要参加者获2000年和2002年江苏省科技进步奖一等奖各1项，2001年中国高校科技奖自然科学二等奖，1998年教育部科技进步一等奖和二等奖各1项。另获2004年江苏省教学成果二等奖1项，IEEE国际会议最佳论文奖3次(IEEE APCCAS'2000、IEEE IWVDVT’2005和IEEE ICNNSP’2008)。

五、任课教师信息（包括主讲教师）：

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）

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

This course will focus on the introduction of advanced theories and techniques in the state-of-the-art research of signal and information processing, and digital communications. It will provide students with the basic theory, analyzing tools and design methods in the field of Advanced Signal Analysis and signal Processing. Students will graspthe research trends and hot research topics of advanced signal processing, so that they could have the ability of conducting the crossing scientific research and solving the practically technical problems independently. In addition, the students are request to have the ability of innovation in some research topics. Understanding of the theoretical foundations of advanced signal processing theory will be achieved through a combination of lecture, seminar, student projects, and computer-based homework assignments.

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

Chapter 1  Parameter Estimations

1.1 Performance bounds of parameter estimation

1.2 Sample mean and sample autocorrelation

1.3 Least squares (LS) estimation

1.4 Linear minimum mean squares estimation (LMMSE)

1.5 Maximum likelihood (ML) estimation and EM algorithms

1.6 Bayes estimation

Chapter 2Optimal Filtering

2.1 Filtering, prediction, deconvolution and noise cancellation

2.2 FIR Wiener filtering

2.3 IIR Wiener filtering

2.4 Kalman filtering－Optimal linear sequential Bayes filtering

2.5 Particle filtering－Sequential Monte Carlo Bayes filtering

Chapter 3  Adaptive filters

3.1 LMS-type adaptive filters

3.2 The basic RLS adaptive filter and its fast algorithms

3.3 RLS adaptive filters based on QR decomposition

3.4 Non-linear adaptive filters

3.5 The applications of adaptive filtering

Chapter 4Array Signal Processing

4.1 Introduction

4.2 The basic beamforming methods

1) min MSE;                    2) max SINR;

3) LCMV(Linear Constrained Minimum Variance);   4) Generalized Sidelobe Canceler

4.3 Adaptive beamforming

1) Sample matrix inversion (SMI, or DMI) and adaptive filtering methods;

2) The projection methods;        3) Adaptive beamforming based on eigen-space

4.4 Direction of Arrival (DOA) estimation

1) Classical spatial spectrum estimation methods;      

2) MVDR methos;                   3) ML estimation methos

4) MUSIC-type estimators;            5) ESPRIT-type estimators

Chapter 5Signal Processing in Communications

5.1 Channel equalizers in communications

1) Linear equalizer (ZF, MMSE);        

2) Non-linear Decision Feedback Equalizer(DFE);

3) Frequency Domain Equalizer (FDE);    

4) Fractional spaced equalizer(FSE): Linear FSE, DFE-FSE, and FD-FSE

5) Maximum Likelihood sequence estimation (MLSE) equalizer

5.2 Channel estimations in communications

1) LS channel estimation based on training sequence;     

2) channel estimation based on superimposed training sequence;

3) channel estimations in OFDMcommunication systems

5.3 Space-Time Signal Processing in MIMO Communications

1) Introduction of MIMO Space-Time Processing;       

2) No CSIT: Space-Time transmit diversity scheme, such as Space-Time coding, time-delay diversity, etc.;

3) No CSIT: Spatial Multiplexing, such as BLASTs;

4) CSIT: Closed-loop MIMO technique, such as Transmit Beamforming, Transmit Precodings;

5) Precodings based on limited feedback

5.4 Signal Processing in Multiuser Communication Systems

1) OFDMA, SDMA, TDMA and CDMA;

2) Multiuser MIMO Precoding schems;          

3) Opportunistic Multi-beamforming and multiuser diversity;

5.5 Signal Processing in Cooperative Communications

Chapter 6Multi-rate Digital Signal Processing and Filter Banks

6.1 The sampling rate alteration

1) Factor-of-M down-sampling;           2) Factor-of-L up-sampling;

3) Fractional sampling rate alteration

6.2 Cascade equivalence of the basic sampling rate alteration devices

6.3 Multistage design of Decimator and Interpolator

6.4 The polyphase decomposition structures

1) FIR filter structures based on the polyphase decomposition

2) Efficient implementation of Decimator and Interpolator based on polyphase decomposition

6.5 Digital filter banks

1) Uniform DFT filter banks and their polyphase implementations;

2) Lth-band filters;

3) Two-channel filter banks and their optimal design;

4) L-channel filter banks (Cosine-modulated filter banks)

Chapter 7  Time-Frequency Analysis

7.1 Short-time Fourier transform of analogue signals

7.2 Short-time Fourier transform of digital signals

7.3 Wigner-Ville distribution and its variations

7.4 Ambiguity functions and Cohen’s class of distributions

7.5 Applications of Time-Frequency Analysis

Chapter 8Blind Signal Processing and Blind Source Separation

8.1 Blind source separation based on information theory criteria

8.2 Blind source separation based on high-order statistics criteria

8.3 ICA and blind signal extraction

8.4 Under-determined blind source separation

8.5 Blind source separation with non-linear mixtures

Chapter 9Blind Channel Estimation and Blind Equalization

9.1 Channel estimation and channel equalization

9.2 Blind Channel Estimation and Equalization based on high-order statistics or second-order cyclostationary

9.3 Blind Channel Estimation and Equalization based on sub-space analysis

9.4 Blind Channel Estimation and Equalization based on linear prediction

9.5 Blind Estimation and Equalization of MIMO Channels

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:

Luxi Yang, male, was born in April, 1964. Hereceived the M.S. and Ph. D. degree in electrical engineering, from the Southeast University, Nanjing, China, in 1990 and 1993, respectively. Since 1993, he has been with the Department of Radio Engineering, Southeast University, where he is currently a Professor of information systems and communications and the director of Digital Signal Processing Division, and also served as a doctoral students advisor. His current research interests include signal processing for wireless communications, MIMO communications, cooperative relaying systems, and statistical signal processing. He is the author or coauthor of two published books and more than 160 journal papers, and holds 20 patents. Prof. Yang received the first- and second-class prizes of Science and Technology Progress Awards of the State Education Ministry of China for 3 times, and the first-class prizes of Science and Technology Progress Awards of Jiang-su Province of China for 2 times. He is currently a Member of Signal Processing Committee of Chinese Institute of Electronics, Chapter Chair of Signal Processing, IEEE Nanjing Section.

5. Lecturer Information (include chief lecturer)