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Underdetermined Mixing Matrix Estimation Algorithm Based on Single Source Points

机译：基于单源点的欠定混合矩阵估计算法

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摘要

This paper considers the mixing matrix estimation in the underdetermined blind source separation. An effective estimation algorithm based on local directional density detection (LDDD) and dynamic data field clustering (DDFC) is proposed. First, argument-based time-frequency single source points detection is employed to improve signal sparsity. To overcome the limitation of traditional clustering algorithms, which depend on the preset of initial clustering centers and the number of sources, the LDDD is introduced to choose the single source points with high potential energy as representative objects to form data preliminary classification. Then DDFC algorithm is adopted to move and merge the representative objects until all column vectors of mixing matrix are estimated. Simulation results show that the proposed method can effectively estimate mixing matrix with high accuracy, especially in the real non-cooperative cases where the number of sources is unknown.
机译：本文在不确定的盲源分离中考虑混合矩阵估计。提出了一种基于局部方向密度检测（LDDD）和动态数据域聚类（DDFC）的有效估计算法。首先，基于参数的时频单源点检测被用来提高信号稀疏度。为了克服传统聚类算法的局限性（取决于初始聚类中心的预设和源数量），引入LDDD选择具有高势能的单个源点作为代表对象，以形成数据初步分类。然后采用DDFC算法移动并合并代表对象，直到估计混合矩阵的所有列向量为止。仿真结果表明，该方法可以有效地估计混合矩阵的精度，特别是在源数未知的非合作实际情况下。

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