关键词: 经验模态分解, 本征模函数, 本征模函数能量谱, 特征提取, 支持向量机（SVM）分类器

Abstract: Empirical Mode Decomposition（EMD） is a method of signal analysis for processing nonlinear and non-stationary signal. EMD can decompose out different time scale of local feature from the original signal. And then the Intrinsic Mode Function（IMF） of those local characteristic signals is got. A new feature extraction and selection method of underwater acoustic signals based on energy spectrum of Intrinsic Mode Function（IMF’s） is presented, where these intrinsic mode components are decomposed via empirical mode decomposition from original signals and transformed into energy spectrum feature vectors, and thus the different signals’ energy spectrum features of sub-band frequency can be inspected. Support Vector Machine（SVM） classifier is used for classification experiments. The results show that the correct identification ratio of experiments based on IMF’s energy spectrum is above 88%, which is superior to feature extraction of wavelet energy spectrum.

Key words: Empirical Mode Decomposition（EMD）, Intrinsic Mode Function（IMF）, Intrinsic Mode Function（IMF） energy spectrum, feature extraction, Support Vector Machine（SVM） classifier