欠定条件下弱稀疏源信号混合矩阵盲估计

针对源信号的稀疏性影响欠定混合矩阵的估计精度，在源信号单源频率及非单源频率分量分析的基础上，通过对观测信号频率峰值的幅值比值所构成的列向量聚类，提出欠定条件下弱稀疏源信号混合矩阵的盲估计方法。鉴于经典聚类算法的局部收敛性带来聚类结果的不稳定性，采用全局收敛特性较好的遗传模拟退火聚类算法提高聚类结果的鲁棒性。仿真实验表明，本文提出的混合矩阵估计方法及采用的聚类算法在不同欠定条件及噪声环境下具有较强的估计性能。

Blind Estimation of Mixing Matrix for Little Sparse Sources in Underdetermined Mixtures

The estimation accuracy of the mixing matrix is influenced by the sources sparsity in the underdetermined mixtures. Based on the analytical results of the single and non single frequencies for source signals, through clustering the column vectors composed by the ratios between the observation signal frequency amplitudes, a new method for the mixing matrix estimation is proposed when the sources are little sparse to each other. Considering the non stability brought by the partial convergence of the classical clustering algorithm, the genetic and simulated annealing clustering algorithm possessing the global convergence characteristic is used to prove the robustness of the clustering result. The experiment results show that the proposed estimation method and the clustering algorithm can provide good estimation performance under different underdetermined conditions and different noises.