A robust geometrical method for blind separation of noisy mixtures of non-negatives sources
Résumé
Recently, we proposed an effective geometrical method for separating linear instantaneous mixtures of non-negative sources, termed Simplicial Cone Shrinking Algorithm for Unmixing Non-negative Sources (SCSA-UNS). The latter method operates in noiseless case, and estimates the mixing matrix and the sources by finding the minimum aperture simplicial cone, containing the scatter plot of mixed data. In this paper, we propose an extension of SCSA-UNS, to tackle the noisy mixtures, in the case where the sparsity degrees of the sources are known a priori. The idea is to progressively eliminate, the noisy mixed data points which are likely to significantly modify the scatter plot of noiseless mixed data and to lead to a bad estimation of the mixing matrix and the sources. Simulations on synthetic data show the effectiveness of the proposed method.
Mots clés
linear instantaneous mixture separation
simplicial cone shrinking algorithm-unmixing nonnegative sources
SCSA-UNS
mixing matrix estimates
minimum aperture simplicial cone
mixed data scatter plot
source sparsity degree
noiseless mixed data
Noise measurement
Equations
Robustness
Mathematical model
Signal to noise ratio
blind source separation
matrix algebra
noisy mixtures
nonnegative source