摘要
为实现低信噪比情况下的舰船轴频电场特征提取,提出一种基于MEFD-小波阈值的特征提取方法.首先采用引入自回归滑动平均模型(ARMA)的改进经验傅里叶分解(MEFD)分解含噪信号;然后采用L2范数筛选出有效信息分量;最后对后续的信号采用小波阈值降噪进行处理.为验证所提方法的有效性,采用仿真信号与船模实测信号进行对比实验.结果表明:本文方法对环境噪声干扰具有鲁棒性,在船模实验2B距离下的提取结果正交性指数为0.0015,相似性指数为0.4503,具有更好的特征提取效果,能实现更远距离的电场信号检测,为后续的电场特性分析及应用打下良好基础.
To realize feature extraction of ship shaft-rate electric field at low signal-noise ratio(SNR),a feature extraction method based on modified empirical Fourier decomposition(MEFD)-wavelet threshold was proposed.First,the MEFD which introduced the auto regressive and moving average model(ARMA)was adopted to separate signal from noise.Then,the L2-norm was calculated to screen out the efficient information components.Finally,the following components were processed by wavelet threshold denoising.To verify the feasibility of the proposed method,the simulation signatures and ship model measured signatures were conducted.Experimental results show that the proposed method is robust to the environmental noise.The index of orthogonality is 0.0015,and similarity index is 0.4503 in the extracting result of 2B distance in ship model experiment,which has better performance of feature extraction than other methods.The proposed method can detect the electric field signatures in a longer distance and lay a good foundation for the subsequent analysis and application of electric field characteristics.
作者
胡育诚
王向军
汪石川
HU Yucheng;WANG Xiangjun;WANG Shichuan(College of Electrical Engineering,Naval University of Engineering,Wuhan 430033,China)
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2024年第4期88-93,共6页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(41476153)。
关键词
轴频电场
改进经验傅里叶分解
小波阈值去噪
L2范数
特征提取
shaft-rate electric field
modified empirical Fourier decomposition
wavelet threshold denoising
L2 norm
feature extraction
