ECE 513
Vector Space Signal Processing

Displaying course information from Spring 2014.

Section Type Times Days Location Instructor
D LEC 0930 - 1050 M W   168 Everitt Lab  Mark Hasegawa-Johnson
Web Page
Official Description Mathematical tools in a vector space framework, including: finite and infinite dimensional vector spaces, Hilbert spaces, orthogonal projections, subspace techniques, least-squares methods, matrix decomposition, conditioning and regularizations, bases and frames, the Hilbert space of random variables, random processes, iterative methods; applications in signal processing, including inverse problems, filter design, sampling, interpolation, sensor array processing, and signal and spectral estimation. Course Information: Prerequisite: ECE 310, ECE 313, and MATH 415.
Subject Area Signal Processing
Course Prerequisites Credit in ECE 313 or STAT 410
Credit in ECE 410
Credit in MATH 415
Course Directors Yoram Bresler
Detailed Description and Outline


  • Matrix inversion: orthogonal projections; left and right inverses; minimum-norm least squares solutions; Moore-Penrose pseudoinverse; reularization; singular value decomposition; Eckart and Young theorem; total least squares; principal components analysis
  • Projections in Hilbert space: Hilbert space; projection theorem; normal equations, approximation and Fourier series; pseudoinverse operators, application to extrapolation of bandlimited sequences
  • Hilbert space of random variables: spectral representation of discrete-time stochastic processes; spectral factorization; linear minimum-variance estimation; discrete-time Wiener filter; innovations representation; Wold decomposition; Gauss Markov theorem; sequential least squares; discrete-time Kalman filter
  • Power spectrum estimation: system identification; Prony's linear prediction method; Fourier and other nonparametric methods of spectrum estimation; resolution limits and model based methods; autoregressive models and the maximum entropy method; Levinson's algorithm; lattice filters; harmonic retrieval by Pisarenko's method; direction finding with passive multi-sensor arrays
Class notes.

B. Porat, Digital Processing of Random Signals, Prentice-Hall, 1994.

Last updated: 2/13/2013