Spring 2016 textbook list
The Spring 2016 ECE textbook list is available online for students.
Current Prerequisites & Course Offering
For current prerequisites for a particular course, and to view course offerings for a particular semester, see the Virginia Tech Course Timetables.
ECE 6624 Spectral Estimation & Modeling (3C)
An advanced introduction to the processing and modeling of random discrete-time signals. Random time series, auto- and cross-correlation sequences and their generation, filtering of random sequences, Wiener filter, matched filters, modeling assumption errors, one-step predictors, rational modeling of random sequences, parametric and non-parametric spectral estimation.
Digital signal processing algorithms find application in a large variety of situations. Many of these applications deal with signals about which there is a degree of uncertainty, so that digital signal processing has to be combined with probabilistic aspects for discrete time stochastic processes. Digital signal processing algorithms for random sequences are designed on the basis of spectral knowledge that most often has to be arrived at by estimation. The parametric and non-parametric estimators become an important aspect of the design process.
Typically offered: Fall. Program Area: Communications.
Prerequisites: 5604, 4624.
The prerequisite material consists of the basic tools for characterizing and processing random signals, as covered in ECE 5605, and the basic tools for deterministic digital signal processing and their application to digital filter design and Discrete Fourier analysis, as treated in ECE 4624.
Department Syllabus Information:Major Measurable Learning Objectives:
- Characterize signals in terms of spectral models
- Design optimal filters, such as Wiener and matched filters, from given information
- Recognize which spectral information is needed for a given application, and derive estimates for that information from data records
- Contrast parametric and non-parametric spectral estimation methods
- Apply spectral modeling techniques and evaluate their appropriateness for the observed data
|1. Random time series, correlation, spectral density||10%|
|2. Linear system response characterization, spectral factorization, whitening||15%|
|3. Covariance generation for ARMA systems and its applications||10%|
|4. Matched and Wiener filtering, orthogonality, smoothing, prediction, one-step prediction||10%|
|5. Fast algorithms: Durbin and Levinson recursions||10%|
|6. Periodogram, resolution, MA estimation, correlation, smoothed and averaged periodogram, Barlett/Welch||15%|
|7. Rational modeling: AR and ARMA, ladder structures, reflection coefficients, maximum entropy||15%|
|8. Model choice, order estimation||5%|
|9. Covariance sequence/matrix parameterization and its applications||10%|