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Advanced Signal Processing

ProgramTeacherCreditDuration

Electronics

Xiangdong Huang

3

48

Course Name: Advanced Signal Processing

Course Code: S2293046

Semester: 3

Credit: 3

Program: Electronics

Course Module: Compulsory

Responsible: Xiangdong Huang

E-mail: xdhuang@tju.edu.cn

Department:School of Electrical and Information Engineering, Tianjin University

TimeAllocation(1 credit hour = 45 minutes)

Exercise

Lecture

Lab-study

Project

Internship

(days)

Personal Work

15

18

15

0

0

20

Course Description

  • Advanced Filtering Theory: Wiener filtering; Adaptive filtering (including Kalman filtering, LMS filtering, RLS filtering etc.).

  • Advanced Spectral Analysis Theory: Eigenvalue Analysis and Principal Component Analysis (including MUSIC and ESPRIT spectral analyzer, etc.), Modern spectral analysis.

  • Blind Signal Processing Theory: Blind de-convolution, Blind Signal Separation, Blind Equalization, etc.

Prerequisite

Digital Signal Processing; Signal and Systems.

Course Objectives

The aim of this course is to give the students a general viewpoint and research idea of advanced signal processing, which helps them solve problems of information extraction in various application fields, such as communication, biomedical engineering, mechanical diagnosis, optical engineering, etc.

Mathematical modeling and statistical analysis are two basic approaches to advanced signal processing. This course will help students build up these two ideas in their minds, which contributes to the grasping of the essence of Wiener filtering, Kalman filtering, adaptive filtering, modern spectral analysis, state space based spectral analysis, de-convolution etc.

After taking this course, students will be able to apply the theory and algorithm of advanced signal processing to solve the aforementioned engineering problems through programming MATLAB or C codes. In return, they should also do further research to develop theory and algorithm to widen the existing discipline of advanced signal processing.

Course Syllabus

  1. Introduction.

  2. Least Square Error Wiener-Kolmogorov Filters.

Least Square Error Estimation: Wiener-Kolmogorov Filter.

  1. Block-Data Formulation of the Wiener Filter.

  2. Interpretation of Wiener Filter as Projection in Vector Space.

  1. Adaptive Filters: Kalman, RLS, LMS.

    1. State-Space Kalman Filters.

    2. Extended Kalman Filter (EFK).

    3. Sample Adaptive Filters – LMS, RLS.

    4. Recursive Least Square (RLS) Adaptive Filters.

  2. Eigenvalue Analysis and Principal Component Analysis.

    1. Introduction – Linear Systems and Eigen Analysis.

    2. Eigen Vectors and Eigenvalues in MUSIC and ESPRIT.

    3. Principal Component Analysis (PCA).

  3. Power Spectrum Analysis.

    1. Power Spectrum and Correlation.

    2. Fourier Series: Representation of Periodic Signals.

    3. Non-Parametric Power Spectrum Estimation.

    4. Model-Based Power Spectrum Estimation.

    5. High-Resolution Spectral Estimation Based on Subspace Eigen-Analysis.

  4. Channel Equalization and Blind Deconvolution.

    1. Blind Equalization Using Channel Input Power Spectrum.

    2. Bayesian Blind Deconvolution and Equalization.

    3. Blind Equalization for Digital Communication Channels.

Textbooks & References

  • Saeed V. Vaseghi.Advanced Digital Signal Processing and Noise Reduction(4th ed). John Wiley&Sons, Ltd., 2008. ISBN: 978-0-470-75406-1.

  • We may also use readings from a few textbooks:

  • S. K. Mitra and Y. Kuo.Digital signal processing: a computer-based approach. McGraw-Hill New York, 2006, vol. 2.

  • A. V. Oppenheim and R. W. Schafer.Discrete-time signal processing (3rd ed). Prentice-hall Englewood Cliffs, 2010.

Grade Distribution

Attendance: 40% Final Exam: 60%

Capability Tasks

CT1: To understand basic science, and to have analytical ability and the ability to integrate related knowledge.

CT2: To apply relevant professional knowledge to the field of science and technology: understanding of the basic concepts and its connotation, application of different methods and concepts which have been learned, capability of judging the scope and limitations of such applications.

CT3: To grasp methodologies and engineering tools: identifying, utilizing and solving problems. Even if the students are not familiar with the content, they can turn to computer tools for systematic analysis.

CT4: To carry out experiments in research environment with the abilities to utilize tools, especially for data collection and processing.

CT10: To have the capacity to work in international environment; the capability to master one or more foreign languages and be open to foreign cultures; be able to acclimatize themselves to the international language environment.

Achievements

  • To solve problems of information extraction. - Level: M.

  • To use Wiener filtering, Kalman filtering, adaptive filtering, modern spectral analysis, and state space based spectral analysis, de-convolution, etc. in modeling and analysis. - Level: M.

  • To be able to apply the theory and algorithm of advanced signal processing to solve the aforementioned engineering problems through programming MATLAB or C codes. - Level: M.

  • To develop deep theory and algorithm to widen the existing discipline of advanced signal processing. - Level: A.

Students: Electronics, Year 2