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.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2010年第6期19-23,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
国家"973"计划项目(2010CB731800)
国家自然科学基金资助项目(60774094
60874061)
国家自然科学基金重点项目(U0635001
U0835003)
关键词
盲源分离
欠定
稀疏表示
非完全稀疏性
blind source separation
underdetermination
sparse representation
incomplete sparsity
