[1]周双红,王玲玲.多特征值分解的稀疏混沌信号盲源分离算法研究[J].智能系统学报,2018,13(05):843-847.[doi:10.11992/tis.201703032]
 ZHOU Shuanghong,WANG Lingling.Research on multi-eigenvalue decomposition blind source separation algorithm for sparse chaotic signals[J].CAAI Transactions on Intelligent Systems,2018,13(05):843-847.[doi:10.11992/tis.201703032]
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多特征值分解的稀疏混沌信号盲源分离算法研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年05期
页码:
843-847
栏目:
出版日期:
2018-09-05

文章信息/Info

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 signalsblind source separationphase spaceseparation matrixparticle swarm optimizationmulti-eigenvalue decompositionminimum mutual information methodmaximum likelihood estimationindependent 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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-03-23。
基金项目:中央高校基础科研业务费(GK2110260178).
作者简介:周双红,男,1981年生,讲师,主要研究方向为盲源分离和电磁兼容;王玲玲,女,1994年生,硕士研究生,主要研究方向为小波分析与优化算法。
通讯作者:王玲玲.E-mail:1325553885@qq.com.
更新日期/Last Update: 2018-10-25