摘要
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.
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 Compo- nent 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.
基金
Sponsored by the Provincial or Ministry Level Pre-research(Grant No. 914A220309090C0201)
