作　　者： ; ; ;

机构地区： 华南理工大学电子与信息学院

出　　处： 《华南理工大学学报（自然科学版）》 2010年第6期19-23,共5页

摘　　要： 由于现有的盲源分离(BSS)算法仅适用于稀疏源而不适用于非完全稀疏源,文中针对两个观测信号,提出了统计非稀疏准则(SNSDP).该准则将信号分成若干区间,根据源的相关性判断各区间是否非完全稀疏,并在非完全稀疏和稀疏的区间采取不同的源恢复策略,因此改善了所估计的源.仿真结果表明,文中算法与传统算法相比,明显改善了恢复信号的波形,提高了信干比. As the existing algorithms of blind source separation（BSS） are suitable only for sparse sources but not for incompletely-sparse sources,a statistically non-sparse decomposition principle（SNSDP） is proposed for two observed signals.In this algorithm,the signals are divided into several intervals and the incomplete sparsity of the intervals is determined according to the correlativity of the sources.By utilizing different source recovery strategies for both the sparse and the incompletely-sparse intervals,the estimated sources can be improved.Simulated results indicate that,as compared with the traditional algorithms,the proposed algorithm greatly improves the waveforms of recovered signals and increases the signal-to-interference ratio.

关 键 词： 盲源分离 欠定 稀疏表示 非完全稀疏性

领　　域： [电子电信] [电子电信]