Due to the lack of sufficient prior information, how to estimate the mixing matrix in multiple underdetermined blind source separation (UBSS) models is a difficult problem. This study proposes an algorithm, which is used for the estimation of mixing matrix in instantaneous UBSS. Firstly, we propose an efficient single-source-points detection criterion with the transformation for the mixed signal vector, which is used as the basis for the clustering process. There are some shortcomings in the classical clustering algorithms, including the dependence on input parameters, restrictions on the data dimension, the requirement of the prior knowledge of the source signals and high complexity. To overcome these drawbacks, the modified density peaks clustering algorithm is used for the estimation of the initial clustering centers to adapt to different circumstances. Based on the idea of mean clustering, the single source points near each initial cluster center are processed, respectively, and the final estimation results of the mixing matrix are obtained. A variety of simulation experiments demonstrate the universality and validity of the proposed algorithm. The proposed method also has excellent performance even under the circumstance of low signal-to-noise ratio.

中文翻译：

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一种新的瞬时欠定盲源分离混合矩阵估计算法

由于缺乏足够的先验信息，如何估计多个欠定盲源分离（UBSS）模型中的混合矩阵是一个难题。本研究提出一种算法，用于瞬时UBSS中的混合矩阵估计。首先，我们提出了一种有效的单源点检测标准，其中包含对混合信号向量的变换，用作聚类过程的基础。经典聚类算法存在对输入参数的依赖、对数据维度的限制、对源信号先验知识的要求以及复杂度高等缺点。为了克服这些缺点，改进的密度峰值聚类算法用于初始聚类中心的估计以适应不同的情况。基于均值聚类的思想，对每个初始聚类中心附近的单个源点分别进行处理，得到混合矩阵的最终估计结果。各种仿真实验证明了所提算法的通用性和有效性。即使在低信噪比的情况下，所提出的方法也具有优异的性能。