| Home > Publications > Reports > Numerical Analysis and Applied Mathematics (TW) |
TW 430
Nicola Mastronardi, Marc Van Barel, and Raf Vandebril
A fast algorithm for subspace tracking
Abstract
Fast estimation and tracking of the principal subspace of a sequence of random vectors is a classic problem, widely encountered in areas such as radar, sonar and speech processing, data compression, data filtering, parameter estimation, pattern recognition, neural analysis, wireless communications, to name just a few.
Among the most robust algorithms for subspace tracking there are the so called OPERA-based algorithms with computational complexity 2nr ² + O(r ²) where n is the input vector dimension and r, n >> r is the desired number of eigencomponents.
In this paper we propose a fast and stable algorithm for subspace tracking based on the EVD-OPERA algorithm with 6nr + 15 r ² computational complexity.
report.pdf (142K) / mailto: M. Van Barel