基于相似度检测的欠定混合矩阵盲估计

针对现有欠定盲分离混合矩阵估计方法中存在的估计精度低以及时间复杂度高等缺点,提出一种基于相似度检测的欠定混合矩阵估计方法.该方法能够在没有任何先验信息的条件下自适应地估计出源信号数目以及混合矩阵,而且不需要进行迭代,时间复杂度低.仿真结果表明,与现有的一些混合矩阵估计方法,如改进K-均值聚类法和拉普拉斯势函数法相比,所提出的方法在源信号数目估计准确率、混合矩阵估计精度以及时间复杂度等方面都具有明显优势.

Blind Estimation of Underdetermined Mixing Matrix Based on Similarity Measurement

A new algorithm based on similarity measurement was proposed in order to address the issue of low estimation accuracy and high computational complexity in the existing algorithms for underdetermined mixing matrix blind estimation. The proposed algorithm can estimate the number of source signals and the mixing matrix automatically without any prior information or much iteration. Compared with the existing algorithms such as the K-means clustering and the Laplace's potential function method, the simulations turn out that the proposed has obvious advantages in the estimation accuracy of the number of source sig-nals and the estimation precision of mixing matrix and the computational complexity.