赵安邦,沈广楠,陈阳,周彬,李桂娟.HHT与神经网络在舰船目标特征提取中的应用[J].声学技术,2012,(3):272~276 |
HHT与神经网络在舰船目标特征提取中的应用 |
The application of HHT and neural network in feature extraction of ship targets |
投稿时间:2011-06-09 修订日期:2011-09-29 |
DOI: |
中文关键词: 目标识别|舰船辐射噪声|神经网络|希尔伯特-黄变换|高阶统计量|本征模函数 |
英文关键词: target identification ship radiated noise neural network Hilbert-Huang Transform(HHT) higher-order statistics Intrinsic Mode Function(IMF) |
基金项目:海洋公益性行业科研专项经费资助项目(gz2010001);国家自然科学基金青年基金(51009041) |
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中文摘要: |
目标识别一直是水声领域的关键技术之一.将高阶累积量用于希尔伯特变换特征提取中,通过对舰船目标辐射噪声信号进行采集,得到舰船目标噪声信号,进而提取目标辐射信号各阶模态的相邻平均瞬时频率比、相对标准差、中心频率、平均强度、高阶矩和高阶累积量等作为特征,最终利用BP神经网络来实现对两类舰船目标的分类识别.通过对实际舰船目标噪声进行识别,验证了该舰船目标识别系统具有较好的识别效果. |
英文摘要: |
Target recognition is one of the key techniques in underwater acoustic area.This article uses high-order cu-mulant and Hilbert transform for feature extraction,firstly gets the ship radiated noise from target ships,and then ex-tracts the ratio of average instantaneous frequency between neighboring IMFs,relative standard deviation,center fre-quency,average intensity,high-order moment and high-order cumulant of different orders of IMFn(n=1-8),finally re-cognizes and classifies two types of ship targets through BP neural network.Good recognition effect of this method has been verified through the classification tests for the actual ship radiated noise. |
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