一种分离噪声盲源信号的FastICA改进算法  

A New Improved FastICA Algorithm in Separating Noise Blind Source Signal

作　　者：方艺鹏 周海峰[1,2] 张恩来 林开荣 郑东强[4] 林忠华[4] FANG Yipeng;ZHOU Haifeng;ZHANG Enlai;LIN Kairong;ZHENG Dongqiang;LIN Zhonghua(School of Marine Engineering,Jimei University,Xiamen 361021,China;Fujian Province Key Laboratory of NavalArchitecture and Ocean Engineering,Xiamen 361021,China;Chengyi University College,Jimei University,Xiamen 361021,China;School of Marine Equipment and Mechanical Engineering,Jimei University,Xiamen 361021,China)

机构地区：[1]集美大学轮机工程学院,福建厦门361021 [2]福建省船舶与海洋工程重点实验室,福建厦门361021 [3]集美大学诚毅学院,福建厦门361021 [4]集美大学海洋装备与机械工程学院,福建厦门361021

出　　处：《集美大学学报（自然科学版）》2022年第1期90-96,共7页Journal of Jimei University：Natural Science

基　　金：国家自然科学基金项目(51179074);福建省自然科学基金项目(2021J01839,2018J01495);产学研项目(S20127);福建省教育厅项目(JAT200242,JAT170318)。

摘　　要：大功率机械所产生的非平稳噪声源,用FastICA算法可以有效地分离出噪声信号,但噪声源识别的准确性较低,为此提出了一种分离噪声源的FastICA改进算法。新方法将最大相似准则、优化算法和快速傅里叶变换相结合,对分离信号的不确定性进行了有效校正。经仿真实验验证,新方法能够完整地还原仿真波形信号,表明该改进算法确实可行且准确性较高。Using FastICA algorithm may effectively separate noise signals from non-stationary noise sources produced by high-power machinery,although the accuracy is relatively low in identifying the noise sources.Therefore,a noise source separation method based on the improved FastICA algorithm is proposed.By combining the maximum similarity criterion,optimization algorithm and fast Fourier transformation,the uncertainty of the signal separation is effectively corrected.Simulation experiments prove that the method can completely restore the simulation waveform signal,which shows that the improved algorithm is feasible and accurate.

关 键 词：噪声源识别 FASTICA 最大相似准则 优化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]