摘要
提出一种约束非负矩阵分解方法用于解决欠定盲信号分离问题。非负矩阵分解直接用于求解欠定盲信号分离时,分解结果不唯一,无法正确分离源信号。在基本非负矩阵分解算法基础上,对分解得到的混合矩阵施加行列式约束,保证分解结果的唯一性;对分解得到的源信号同时施加稀疏性约束和最小相关约束,实现混合信号的唯一分解,提高源信号分离性能。仿真实验证明了算法的有效性。
This paper proposed a constrained nonnegative matrix factorization（NMF） method to resolve the problem of underdetermined blind signal separation.It was hard to obtain unique factorization and correctly separated source signals when NMF was directly applied to resolve problem above.On the basis of standard NMF,imposed determinant criterion on estimated mixing matrix to achieve the unique factorization of NMF.Imposed sparsity and the least correlated component constraints on estimated sources to realize the unique separation of mixed signals and improve the performance of source separation.Simulation results show the effectiveness of the proposed algorithm.
出处
《计算机应用研究》
CSCD
北大核心
2011年第5期1843-1845,共3页
Application Research of Computers
基金
国防科技重点实验室基金资助项目(9140C131010109DZ46)
关键词
欠定盲分离
非负矩阵分解
行列式准则
稀疏性
最小相关约束
underdetermined BSS（UBSS）
non-negative matrix factorization（NMF）
determinant criterion
sparsity
the least correlated component constraints
