多特征值分解的稀疏混沌信号盲源分离算法研究

PDF下载HTML

Title:

Research on multi-eigenvalue decomposition blind source separation algorithm for sparse chaotic signals

作者:

周双红, 王玲玲

哈尔滨工程大学 理学院, 黑龙江 哈尔滨 150001

Author(s):

ZHOU Shuanghong, WANG Lingling

College of Science, Harbin Engineering University, Harbin 150001, China

关键词:

混沌信号; 盲源分离; 相位空间; 分离矩阵; 粒子群优化算法; 多特征值分解; 最小互信息法; 极大似然估计; 独立分量分析

Keywords:

chaotic signals; blind source separation; phase space; separation matrix; particle swarm optimization; multi-eigenvalue decomposition; minimum mutual information method; maximum likelihood estimation; independent component analysis

分类号:

TP181

DOI:

10.11992/tis.201703032

摘要:

针对受到噪声干扰的激光混沌源信号高精度重构的问题，本文提出了一种基于相位空间重构混沌流信号的盲源分离算法。该算法首先对分离信号的相位空间进行时间延迟重构，然后将分离矩阵作为待优化参数，通过在相空间中构建目标函数，将盲源分离问题转换为优化问题，应用粒子群优化算法求解最优分离矩阵，进而将观测数据乘以最优分离矩阵来重构源信号。实验结果表明，该算法不仅具有快速收敛的特点，其精度明显优于各种噪声强度下现有的独立分量分析方法。

Abstract:

To perform high-precision restructuring of chaotic laser-source signals that are experiencing noise interference, in this paper, we propose a blind-source-separation algorithm based on a phase-space-reconstructed chaotic stream signal. This algorithm first performs a time-delay reconstruction of the phase space of separation signals, and then treats the separation matrix as a parameter to be optimized. Then, it converts the blind source separation into an optimization problem by constructing an objective function in the phase space, and solves the optimal separation matrix using a particle swarm optimization algorithm. It then multiplies the observation data by the optimal separation matrix to reconstruct the source signals. Experimental results show that the algorithm achieves rapid convergence, and its accuracy is obviously superior to the existing independent component analysis method under various noise intensities.