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| Abstract: |
| 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 cannot have good performance in many cases. Considering most of signals in real world are band limited signals, a new method based on sub band division is proposed in this paper. Sensing signals are divided into different sub bands by complementary filter firstly. Then, classical Independent Component Analysis (ICA) algorithms are applied in each sub band. Next, based on each sub band’s estimation of mixing matrix, the mixing matrix is estimated with cluster analysis algorithms. After that, the sub band signals are recovered using the estimation mixing matrix, and then, the resource signals are reconstructed by combining the related sub band signals together. This method can recover the source signals if active sources at any sub band do not exceed that of sensors. This is also a well mixing matrix estimating algorithm. Finally, computer simulation confirms the validity and good separation performance of this method. |
| Key words: under determined blind source separations complementary filters cluster analysis |
| DOI：10.11916/j.issn.1005-9113.2012.02.013 |
| Clc Number:TP911 |
| Fund: |
