Aiming at the problem of underdetermined blind identification, we propose a method of blind identification of underdetermined mixtures based on spatial time-frequency distributions (TED). First calculate the spatial time frequency distribution of the mixtures, stack the TFD matrices corresponded to the auto-source time frequency points in a new matrix with higher dimensions, and finally estimate the mixing matrix by simultaneous matrix diagonalization and eigenvalues decomposition. The assumption that the sources are sparse or independent is not necessary for the proposed method. Furthermore, we increase the robustness of the method by detecting the auto-source points with enough energy. Simulation results indicate that the proposed algorithm estimates the mixing matrix with higher accuracy compared to the other algorithms at the same SNR.%针对欠定混合矩阵的盲辨识问题,提出了基于空间时频分布的盲辨识算法,首先计算信号的空间时频分布并找出源信号的自源时频点,然后把所有自源点对应的时频分布矩阵表示成高维矩阵的形式,再通过联合对角化和特征值分解估计出混合矩阵.该方法不需要假设源信号是稀疏的或独立的,此外通过检测能量足够大的自源时频点,提高了算法的鲁棒性.仿真结果表明在相同信噪比条件下与已有算法相比,本文方法提高了混合矩阵的估计精度.
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