Course Syllabus

Course Title: Modern Signal Processing

Lecturer：XIE Rong

Course Textbook: Modern Digital Signal Processing, Cristi Roberto,CL-Engineering (2003-05出版) ISBN: 0534400957

Course Description: 

This course will provide the students with basic principles and computing methods of modern signal processing. It expands the researching fields of signal processing with an emphasis on the analyzing and processing of random signal. It develops a series of new signal analyzing theories, such as modern spectral estimation (ARMA model), hmomorphic filtering, time-frequency analysis (short-time Fourier transform, short time Gabor transform, wavelet transform, Wigner-Ville distribution, suppressing crossterms of time-frequency distribution), and cyclostationary signal processing.

Prerequisites: Digital Signal Processing, matrix theory, and linear algebra.

Outline:

1.     Foundations of the discrete signal

²  Mathematical basics of random signal

²  Discrete signal modeling

²  Basic methods of Signal estimations: unbiased estimation, consistent estimation, Bias of estimator, unbiased miminum variance, Cramer-Rao inequation, Bayes Estimation, Linear square estimation, Maximum Likelihood Estimation(MLE), Methods of Least Squares, regression models, a time series model

2.     Modern spectral estimation

²  Periodogram method, Blackman-Tukey method

²  Yule-Walker equations

²  Levinson-Durbin(L-D) Method

²  AutoRegressive method

3.     Homomorphic filtering

4.     Non-Gaussian random signal processing

²  Higher-order cumulant

²  Bispectrum & trispectrum Analysis

5.     Time-Frequency Analysis

²  Short time Fourier transform

²  Short time Gabor transform

²  Wigner-Ville Distribution

²  Suppressing Crossterms of Time-Frequency Distribution

6.     Cyclostationary signal processing

²  Cyclic Spectrum estimations