ENEE 634: Space-Time Signal Processing
Course Goals:
This course considers space-time processing aspects of signal processing,
including fast algorithms, numerical computation, adaptive beamforming, direction of arrival
estimation, array processing, adaptive algorithms, channel equalization, blind equalization
and identification, and space-time coding, modulation, and MIMO communications and
signal processing.
Course Prerequisite(s):
ENEE 620, ENEE 630.
Topics Prerequisite(s):
Textbook(s)
- D.G. Manolakis, V.K. Ingle, and S.M. Kogon, Statistical and Adaptive Signal Processing,
McGraw Hill, 2000.
- S. Haykin, Adaptive Filter Theory, 3nd Ed, Prentice-Hall, 1996.
Reference(s):
Core Topics:
- Matrix computations and numerical analysis for signal processing: eigen-decomposition,
orthogonal decompositions and algorithms, singular value decomposition, method of
least-squares
- Array Processing (Spatial Processing):
- Sensor array and signal model;
- Beamforming: linearly constrained minimum-variance beamformers, sidelobe cancelers,
interference cancelation;
- Direction of Arrivals Estimation: MUSIC algorithm, ESPRIT algorithm, subspace
fitting
- Adaptive Signal Processing (Temporal Processing):
- Least mean square algorithm: steepest descent, error behavior, convergence analysis;
- Recursive least-squares algorithms: standard RLS, QRD-LSL filters
- Blind Equalization and Identification (Unsupervised Adaptive Processing):
- constant-modulus algorithm;
- fractionally spaced equalizers;
- subspace-based blind equalization
- Space-Time Signal Processing and Communications:
- Wireless channel characteristics and models;
- smart antennas for wireless communications;
- space-time coding;
- channel estimation;
- multiple-input multiple-output communications and signal processing
Optional Topics:
Course Structure:
Grading Method:
|
|