直接正交联合对角化盲分离算法
发布时间：2019-06-13 12:36
【摘要】：针对盲源分离问题,利用白化预处理后信号的二阶和高阶累积量矩阵具有正交联合对角化的结构性质,以及多个实对称矩阵具有相同特征向量即可同时正交对角化的实对称矩阵的特征分解的理论,提出一种基于部分累积量矩阵特征分解的直接正交联合对角化算法。该算法仅需要部分累积量矩阵信息,从而大大降低计算过程中的存储量和计算量。通过数值模拟,该算法和经典的JADE算法性能接近,可以有效地进行盲分离。
[Abstract]:In order to solve the problem of blind source separation, a direct orthogonal joint diagonalization algorithm based on eigendecomposition of partial cumulant matrix is proposed by using the structural property of orthogonal joint diagonalization of second-order and high-order cumulant matrices after whitening preprocessing, and the theory of eigendecomposition of real symmetric matrices with the same eigenvector. The algorithm only needs partial cumulant matrix information, which greatly reduces the amount of storage and computation in the calculation process. Through numerical simulation, the performance of the algorithm is close to that of the classical JADE algorithm, and it can be used for blind separation effectively.
【作者单位】： 电子科技大学数学科学学院;
【基金】：国家自然科学基金(11301056)
【分类号】：TN911.7
[Abstract]:In order to solve the problem of blind source separation, a direct orthogonal joint diagonalization algorithm based on eigendecomposition of partial cumulant matrix is proposed by using the structural property of orthogonal joint diagonalization of second-order and high-order cumulant matrices after whitening preprocessing, and the theory of eigendecomposition of real symmetric matrices with the same eigenvector. The algorithm only needs partial cumulant matrix information, which greatly reduces the amount of storage and computation in the calculation process. Through numerical simulation, the performance of the algorithm is close to that of the classical JADE algorithm, and it can be used for blind separation effectively.
【作者单位】： 电子科技大学数学科学学院;
【基金】：国家自然科学基金(11301056)
【分类号】：TN911.7
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