摘要
经验模态分解(EMD)是一种新的非平稳时变信号处理方法,可以自适应地将信号的局部特征逐级分解出来。提出了基于EMD的舰船噪声特征提取与选择方法,将本征模态函数(IM F)分量及其瞬时频率作为特征,并选择其判别熵作为特征向量的可分性度量。数值仿真和实际噪声数据处理的结果表明IM F分量和频率可以充分体现目标的特征,具有良好的类别可分性。
Purpose. Ref. 2 pointed out that wavelet transform can not self-adaptively perform feature extraction of non-stationary and time-varying signals. The EMD and Hilbert spectrum method proposed by Huang can self-adaptively deal with such signals. We are interested in Ref. 3＇s method because we have long engaged in feature extraction of non-stationary and time varying signals radiated from target ships. In the full paper, we explain in detail how to use EMD and Hilbert spectrum; in this abstract we just list the three topics of explanation： （A） brief introduction to EMD and Hilbert spectrum, in which IMFs （Intrinsic Mode Functions） are explained; （B） feature extraction and analysis of target signals based on EMD and Hilbert spectrum; under this topic, esq. （7） through （10） in the full paper are derived ; （C） extraction of three types of real signals of target ships; the results are summarized in Table 1 of the full paper. The results in Table 2 indicate that IMFs are effective feature vectors for classification.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2006年第4期453-456,共4页
Journal of Northwestern Polytechnical University
关键词
经验模态分解
特征提取与选择
目标分类
Empirical Mode Decomposition （EMD）, Hilbert spectrum, feature extraction, classification
