DB004103-現代信号分析與處理技術

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）

本課程教學内容主要取材于信号與信息處理、現代數字通信的基礎理論與前沿技術，目的在于使學生較全面地掌握現代信号分析與處理的理論基礎和先進的分析方法與設計技術，并且通過跟蹤本學科的最新發展趨勢和熱門研究課題，培養學生具備适應未來新的交叉學科發展的綜合創新能力，并能靈活應用所學的知識解決相關的實際工程問題，在信息與通信工程領域培養一定的獨立科研工作能力。

本課程采取教師講課、學生專題研究實踐和作報告相結合的方式組織教學。

二、教學大綱（含章節目錄）（可附頁）

第一章參數估計方法

1.1 參數估計的基本性能

1.2 随機信号統計量的樣本估計

1.3 最小二乘估計(LS)

1.4 線性均方估計(LMMSE)

1.5 最大似然估計(ML)與EM算法

1.6  Bayes估計

第二章最優濾波方法

2.1 濾波、預測、反卷積與噪聲抑制

2.2 FIR維納濾波器

2.3 IIR維納濾波器

2.4 卡爾曼濾波器－最優線性序貫貝葉斯濾波

2.5 粒子濾波器－序貫MC貝葉斯濾波

第三章自适應濾波

3.1 LMS類自适應濾波器

3.2基本RLS自适應濾波算法及快速實現

3.3基于QR分解的RLS濾波器

3.4非線性自适應濾波

3.5自适應濾波的應用

第四章陣列信号處理

4.1引言

4.2基本的波束形成方法

1) min MSE;                    2) max SINR;

3) LCMV(線性約束最小方差法);   4) 廣義旁瓣對消器(零點形成技術)

4.3自适應波束形成

1) 采樣矩陣求逆(SMI,或稱DMI,直接求逆)和自适應濾波法;

2)投影變換法;        3)基于特征空間的自适應波束形成

4.4 波達方向(DOA)估計

1) 經典空間譜估計法;        2) MVDR法;      3) 最大似然(ML)法

4) MUSIC類算法;            5) ESPRIT類算法

第五章通信中的信号處理

5.1通信中的信道均衡

1) 線性均衡器(ZF, MMSE);     2)非線性判決反饋均衡器(DFE);

3) 頻域均衡器(FDE);          4) 分數抽頭均衡器(FSE):線性;DFE-FSE;FD-FSE

5) 最大似然序列估計(MLSE)均衡器

5.2通信中的信道估計

1) 基于訓練序列的LS信道估計;     2) 基于疊加訓練序列的信道估計;

3) OFDM通信系統中的信道估計

5.3 MIMO通信中的空時信号處理

1) MIMO空時處理概述;       

2) 無CSIT: 空時發射分集技術,如空時碼、延時分集等;

3) 無CSIT: 空間複用技術, 如BLAST等;

4) 有CSIT: 閉環MIMO技術, 如發射波束形成、預編碼等;

5) 基于有限反饋的預編碼

5.4多用戶通信系統中的信号處理

1) OFDMA、空分多址(SDMA)和其他多址技術;

2) 多用戶預編碼技術;               3) 随機多波束形成與多用戶分集;

5.5 協作通信系統中的信号處理

第六章  多速率數字信号處理與濾波器組

6.1 數字信号的采樣率變換

1) M倍降采樣;

2) L倍升采樣;

3) 分數倍采樣率變換

6.2 多速率處理模塊的級聯等效形式

6.3 抽取器和插值器的多級實現

6.4 多相分解結構

1) 基于多相分解的FIR濾波器實現結構;

2) 升采樣器和降采樣器的高效實現結構

6.5 數字濾波器組：

1) 簡單的最大均勻抽取DFT濾波器組;

2) 多子帶濾波器;

3) 兩通道濾波器組及其優化設計;

4) 多通道濾波器組(餘弦調制濾波器組)

第七章信号的時頻分析

7.1連續時間短時傅裡葉變換

7.2離散信号的短時傅裡葉變換

7.3 Wigner-Ville分布及其變型

7.4模糊度函數和Cohen類時頻分布

7.5時-頻分布的應用實例

第八章盲信号處理與盲源分離

8.1 基于信息論的盲源分離方法

8.2 基于高階統計量逼近的盲源分離算法

8.3 盲信号抽取與ICA分析

8.4 盲信号分離的欠定問題

8.5 非線性混疊信号的盲分離

第九章信道盲估計與盲均衡

9.1 信道估計與信道均衡

9.2 基于高階統計量和二階循環平穩特性的信道盲均衡

9.3 基于子空間分析的信道盲估計與盲均衡

9.4 基于線性預測模型的信道盲估計與盲均衡

9.5 MIMO信道的盲估計與盲均衡

三、教學周曆

（第二章、第七章由王橋主講，其餘由楊綠溪主講）

四、主講教師簡介：

楊綠溪：1964年生，beat365正版唯一beat365正版唯一教授，博士生導師，1993年獲博士學位。近年來主要從事MIMO通信系統設計、協作通信與分集處理、多用戶MIMO方案、有限反饋預編碼等方面的科研工作，已申請發明專利20項，提交3GPP2 UMB國際通信标準提案3份，并在包括IEEE信号處理、通信、電路與系統會刊和中國科學E輯、F輯等國内外刊物與IEEE會議上發表和合作發表論文200多篇，SCI收錄30多篇，EI收錄120多篇。曾擔任國家863項目負責人、國家攀登計劃重大項目子課題組長、國家自然科學基金重點項目課題組長等，參加過國家自然科學基金重大項目的研究；主持過4項國家自然科學基金，和其它10多項省部級科研項目，結題評審均為“優”，其中2項被評為“特優”。曾作為主要參加者獲2000年和2002年江蘇省科技進步獎一等獎各1項，2001年中國高校科技獎自然科學二等獎，1998年教育部科技進步一等獎和二等獎各1項。另獲2004年江蘇省教學成果二等獎1項，IEEE國際會議最佳論文獎3次(IEEE APCCAS'2000、IEEE IWVDVT’2005和IEEE ICNNSP’2008)。

五、任課教師信息（包括主講教師）：

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)