Under-determined blind source separation based on sub-band division

This paper considers the blind source separation in under-determined case, when there are more sources than sensors. So many algorithms based on sparse in some signal representation domain, mostly in Time-Frequency (t-f) domain, are proposed in recent years. However, constrained by window effects and t-f resolution, these algorithms can not have good performances in many cases. Considering most of signals in real world are band-limited signals, a new method based on sub-band division are proposed in this paper. Sensing signals are divided into different sub-bands by Complementary filters first. Then, classical Independent Component Analysis (ICA) algorithms are applied in each sub-band. Next, the mixing matrix is estimated with cluster analysis algorithms based on each sub-band´s estimation of mixing matrix. And last, the sub-band signals are recovered using the estimated mixing matrix and the resource signals are reconstructed by combining the related sub-band signals together. This method could recover the source signals if active sources at any sub-band does not exceed that of sensors. This is also a well mixing matrix estimating algorithm. Finally, computer simulation confirms the validity and good separating performance of this method.