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A empirical mode decomposition approach to feature extraction of ship-radiated noise

机译:一种经验模式分解方法的舰船辐射噪声特征提取

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How to obtain effective, reliable characteristic parameter from the limited measured data is a question of great importance in feature extraction. Based on self-adaptive filter action of empirical mode decomposition (EMD) method, this paper drew statistic centre frequency of spectrum of intrinsic modes as new line spectrum characteristic of underwater acoustic signal, and adopted the law of nearest neighborhood to recognize. The characteristics used in this method include the high-frequency characteristics which other spectral analysis methods neglect, so it can get higher discrimination when ten types of objects are classed despite small sample volume and less data amount.
机译:如何从有限的测量数据中获得有效,可靠的特征参数是特征提取中的重要问题。基于经验模态分解(EMD)方法的自适应滤波作用,本文将本征模态的统计中心频率作为水下声信号的新线谱特征,并采用最近邻定律进行识别。该方法所使用的特性包括其他频谱分析方法所忽略的高频特性,因此尽管样本量小且数据量少,但在分类十种对象时可以得到更高的辨别力。

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