ENEE 630: ADVANCED DIGITAL SIGNAL PROCESSING

Course Goals:

This is the first-year graduate course in signal processing. The objective is to establish fundamental concepts of signal processing on multirate processing, parametric modeling, linear prediction theory, modern spectral estimation, and high-resolution techniques.

Course Prerequisite(s):

ENEE 425 or equivalent; co-requisite ENEE 620 or equivalent.

Topics Prerequisite(s):

Textbook(s)

Reference(s):

  • P. P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice-Hall, 1993.
  • D.G. Manolakis, V.K. Ingle, and S.M. Kogon, Statistical and Adaptive Signal Processing, Mc- Graw Hill, 2000.
  • M. Hayes, Statistical Digital Signal Processing and Modeling, Wiley, 1996.
  • S. Haykin, Adaptive Filter Theory, 2nd Ed, Prentice-Hall, 1991.

Core Topics:

  1. Multirate Signal Processing (Chaps. 2, 4, 5, Vaidyanathan)
    • Decimation and interpolation; sampling rate conversion; direct-form and polyphase representation.
    • Time-varying filter structures; implementation of DFT Filter Banks; multistage implementation of sampling-rate conversion
    • Quadrature mirror filter (QMF) bank; M-channel filter banks; multiresolution filter banks.
    • Perfect reconstruction systems; alias-free filter banks.
    • Multi-resolution analysis.
  2. Parametric Signal Modeling and Linear Prediction Theory (Chaps. 2, 5, 6, Haykin; or chaps. 4, 6, 9, Manolakis et al)
    • Stochastic time-series models: AR, MA, ARMA; Wold decomposition theorem.
    • Discrete Wiener filters: principle of orthogonality, normal equations.
    • Linear prediction theory: forward and backward linear predictions and their properties.
    • Levinson-Durbin algorithm; lattice prediction filter; innovation process; joint-process estimation.
  3. Spectral Estimation (Chaps. 5, 9, Manolakis et al)
    • Nonparametric methods: Periodograms and windowing methods; statistical properties; minimumvariance spectral estimation.
    • Parametric methods: AR, MA, and ARMA spectral estimation; maximum entropy method.
    • Higher-order statistics: parametric and non-parametric approaches.
    • High-resolution techniques: MUSIC algorithm.

Optional Topics:


Course Structure:

Grading Method:

| Dept. of Electrical & Computer Engineering | A. James Clark School of Engineering | University of Maryland |