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Prewhitened Blind Source Separation With Orthogonality Constraints (1999)
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BibTeX
@MISC{Douglas99prewhitenedblind,
author = {S. C. Douglas},
title = {Prewhitened Blind Source Separation With Orthogonality Constraints},
year = {1999}
}
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Abstract
Recently, developments in self-stabilized algorithms for gradient adaptation of orthonormal matrices have resulted in simple but powerful principal and minor subspace analysis methods. In this paper, we extend these ideas to develop algorithms for instantaneous prewhitened blind separation of homogeneous signal mixtures. Our algorithms are proven to be self-stabilizing to the Stiefel manifold of orthonormal matrices, such that the rows of the adaptive demixing matrix do not need to be periodically re-orthonormalized. Several algorithm forms are developed, including those that are equivariant with respect to the prewhitened mixing matrix. Simulations verify the excellent numerical properties of the proposed methods for blind source separation. 1. INTRODUCTION Consider the problem of blind source separation (BSS) [1]{ [11], in which an n-dimensional vector sequence x(k) = [x1(k) x2(k) xn(k)] T is assumed to be produced from an n-dimensional source signal vector s(k) = [s1 (k) s...
Keyphrases
blind source separation orthogonality constraint orthonormal matrix excellent numerical property adaptive demixing matrix introduction consider n-dimensional source signal vector n-dimensional vector sequence gradient adaptation minor subspace analysis method homogeneous signal mixture self-stabilized algorithm mixing matrix stiefel manifold
