一种鲁棒的病态混叠信号欠定盲源分离算法

噪声环境下的病态混叠信号具有较强的空间复共线性，因此基于聚类的稀疏分量分析(SCA)方法难以在欠定条件下对其进行有效的分离。针对这一问题，该文首先建立了噪声环境下病态混叠信号欠定盲源分离问题的数学模型，分析了基于线性聚类的SCA方法在解决该问题时的局限性，提出了一种基于SCA和非正交联合对角化(NJD)的分离算法，该方法利用NJD不要求混叠矩阵为酉矩阵的特性，较好地解决了欠定盲源分离中的病态混叠问题。仿真实验表明，该方法在信号分离效果、噪声鲁棒性以及病态混叠鲁棒性上都明显优于基于启发式聚类粒子群优化的(CGPSO)的SCA方法。

A Robust Underdetermined Blind Sources Separation Algorithm for Ill-conditioned Mixing Signals

Because of the multi-colinearity of the ill-conditioned mixing signals, it is difficult to solve the issue of underdetermined blind sources separation for ill-conditioned mixing signals in noisy environment by Sparse Component Analysis (SCA). The model of the problem is built and the limitation of clustering methods to solve the problem is analyzed in this paper. Then a robust underdetermined blind sources separation algorithm based on SCA and Nonorthogonal Joint Diagonalization (NJD) is presented. NJD has the property that the mixing matrix is not necessarily unitary, which is used to solve the above problem in the novel algorithm. Simulation experiments show that the algorithm can improve the performance in separation performance, noise robust and ill-conditioned mixing robust compared with Cluster Guide Particle Swarm Optimization (CGPSO) algorithm.