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Proceedings Paper

Recursive least-squares-based subspace tracking
Author(s): Bin Yang
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Paper Abstract

In this paper, we introduce a new interpretation of the signal subspace as the solution of an unconstrained minimization problem. We show that recursive least squares techniques can be applied to track the signal subspace recursively by making an appropriate projection approximation of the cost function. The resulting algorithms have a computational complexity of O(nr) where n is the input vector dimension and r(r<n) is the number of desired eigen components. We demonstrate that this approach can also be extended to track the rank, i.e. the number of signals, at the same order of linear (approximately n) computational complexity. Simulation results show that our algorithms offer a comparable and in some cases more robust performance than the spherical tracker by DeGroat, the URV updating by Stewart, and even the exact eigenvalue decomposition.

Paper Details

Date Published: 28 October 1994
PDF: 12 pages
Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); doi: 10.1117/12.190847
Show Author Affiliations
Bin Yang, Ruhr Univ. Bochum (Germany)


Published in SPIE Proceedings Vol. 2296:
Advanced Signal Processing: Algorithms, Architectures, and Implementations V
Franklin T. Luk, Editor(s)

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