基于EMD-NLPCA的欠定非线性盲源分离算法及应用  

Research and application of EMD-NLPCA algorithm

作　　者：唐铭阳 吴亚锋[1] 李晋 TANG Mingyang;WU Yafeng;LI Jin(School of Power and Energy,Northwestern Polytechnical University,Xi′an Shaanxi710129,China)

机构地区：[1]西北工业大学能源与动力学院,陕西西安710129

出　　处：《太赫兹科学与电子信息学报》2024年第2期194-200,共7页Journal of Terahertz Science and Electronic Information Technology

摘　　要：对欠定非线性混合信号的盲源分离算法进行研究,提出一种基于经验模式分解与非线性主成分分析(EMD-NLPCA)的盲源分离算法。首先对观测信号做EMD处理,重构信号后引入高阶统计量,再进行主成分分析,完成信号分离。该算法既可以应对欠定环境,又解决了非线性混合问题。仿真实验中,将该算法与稀疏分量分析法的结果进行比照,证明了该算法的正确性以及相较于稀疏分量分析法更具普适性。将该算法用于无人机发动机开车音频信号的分离,效果较好。A Blind Source Separation(BSS)algorithm based on Empirical Mode Decomposition-Non-Linear Principal Component Analysis(EMD-NLPCA)is proposed after studying the BSS algorithm for underdetermined non-linear mixed signals.Firstly,EMD is applied to the observed signal,then high-order statistics are introduced after reconstructing the signal.The principal component analysis is carried out to complete the signal separation.This algorithm can not only deal with the undetermined environment but also solve the problem of non-linear mixing.In the simulation,the results of the algorithm are compared with those of the sparse component analysis,which proves that the proposed algorithm is correct and more universal than the sparse component analysis.Finally,the algorithm is applied to the separation of driving audio signals of unmanned aerial vehicle engines,and it works well.

关 键 词：盲源分离 经验模式分解 非线性主成分分析 欠定 非线性混合 

分 类 号：TN911.7[电子电信—通信与信息系统]