Dynamic Blind Source Separation Using Subspace Method

Abstract

Blind source separation algorithm is usually not able to estimate the number of unknown signal sources. In many occasions, the number of source signal is unknown and may even be in dynamic changes. This paper has achieved to estimate the number of sources and real-time tracking using subspace method in the over-determined blind source separation, while the number of sources is unknown and dynamic . The first section is the estimation of the rank of signal subspace and the second section is about the subspace tracking algorithm. The subspace method is to separate the observed sensor signals into signal subspace and noise subspace. This will not only greatly reduce the noise, but also can estimate the number of active source signals by the measurement of eigenvalues. To achieve the real-time adjustment of the threshold in the dynamic blind source processing with Akaike’s information criterion (AIC) and the minimum description length criterion (MDL).