COMBINED SUBSPACE TRACKING, PREWHITENING, AND CONTRAST OPTIMIZATION FOR NOISY BLIND SIGNAL SEPARATION
Scott C. Douglas
In many practical blind signal separation (BSS) applica
tions, the measured mixtures contain additive noise that
limits the performances of most existing BSS algorithms.
In this paper, we present three new methods for blindly
extracting independent sources from noisy linear mixtures.
All of the methods combine approximate leastsquares sub
space tracking with contrastbased BSS in an elegant way.
One of the BSS algorithms is designed to perform mini
mum meansquareerror (MMSE) or Wiener estimation of
the unknown sources, and novel leastsquares prewhiten
ing and orthogonal contrast optimization techniques are in
troduced. Simulations verify the robust and accurate be
haviors of the proposed methods for extracting unknown
sources from noisy mixtures.