CAJ | 학술논문

经验模态分解（EMD）是用来处理非平稳时变信号的一种信号分析方法，该方法对所分析信号的局部特征信号进行不同时间尺度的分解，从而得到这些局部特征信号的各阶本征模函数（IMF）。提出了一种基于IMF能量谱的水声信号特征提取与选择方法，通过对水声信号进行经验模态分解，提取信号的本征模式分量并转换为能量谱特征向量，从而观测不同信号子频带能量谱的特征变化。分类实验采用支持向量机（SVM）分类器进行。实验结果表明，相对于小波能量谱特征提取法而言，利用IMF能量谱作为特征向量的分类实验具有更佳的分类效果，平均正确率达88%以上。
경험모태분해（EMD）시용래처리비평은시변신호적일충신호분석방법，해방법대소분석신호적국부특정신호진행불동시간척도적분해，종이득도저사국부특정신호적각계본정모함수（IMF）。제출료일충기우IMF능량보적수성신호특정제취여선택방법，통과대수성신호진행경험모태분해，제취신호적본정모식분량병전환위능량보특정향량，종이관측불동신호자빈대능량보적특정변화。분류실험채용지지향량궤（SVM）분류기진행。실험결과표명，상대우소파능량보특정제취법이언，이용IMF능량보작위특정향량적분류실험구유경가적분류효과，평균정학솔체88%이상。
Empirical Mode Decomposition(EMD)is a method of signal analysis for processing nonlinear and non-sta-tionary 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 under-water 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 identifi-cation ratio of experiments based on IMF’s energy spectrum is above 88%, which is superior to feature extraction of wavelet energy spectrum.