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) ...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.展开更多
为了提高最小支撑正交匹配追踪(least support denosing-orthogonal matching pursuit,LSD-OMP)算法的重构精度,缩短重构时间,改善算法性能,提出一种基于多重支撑的正则化正交匹配追踪(multiple support of regularization orthogonal m...为了提高最小支撑正交匹配追踪(least support denosing-orthogonal matching pursuit,LSD-OMP)算法的重构精度,缩短重构时间,改善算法性能,提出一种基于多重支撑的正则化正交匹配追踪(multiple support of regularization orthogonal matching pursuit,MS-ROMP)算法。由于LSD-OMP算法仅选择一些原子来定位支撑集,并且无法消除添加到支撑集中的错误原子,因此信号恢复精度降低并且重构时间增加。针对此问题,本文通过改进算法终止条件,引入多重支撑和正则化来改善算法性能,即通过设置阈值,剔除一些错误的原子,并组合一些支持集来定位最佳支持集,从混合信号中分离出源信号,从而更加精确的实现欠定盲源分离。仿真实验验证了该算法的有效性。展开更多
基金Sponsored by the Provincial or Ministry Level Pre-research(Grant No. 914A220309090C0201)
文摘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.
文摘为了提高最小支撑正交匹配追踪(least support denosing-orthogonal matching pursuit,LSD-OMP)算法的重构精度,缩短重构时间,改善算法性能,提出一种基于多重支撑的正则化正交匹配追踪(multiple support of regularization orthogonal matching pursuit,MS-ROMP)算法。由于LSD-OMP算法仅选择一些原子来定位支撑集,并且无法消除添加到支撑集中的错误原子,因此信号恢复精度降低并且重构时间增加。针对此问题,本文通过改进算法终止条件,引入多重支撑和正则化来改善算法性能,即通过设置阈值,剔除一些错误的原子,并组合一些支持集来定位最佳支持集,从混合信号中分离出源信号,从而更加精确的实现欠定盲源分离。仿真实验验证了该算法的有效性。
