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基于EMD-1(1/2)维谱的舰船辐射噪声调制特征提取 被引量:3

Modulation Feature Extraction of Ship Radiated Nose Based on EMD 1(1/2)-Dimensional Spectrum
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摘要 舰船辐射噪声的调制信息中包含了大量有关舰船特征的信息,作为被声呐识别和判断的依据,由此判别目标的类型和速度。传统的DEMON(Detection of Envelope Modulation On Noise)谱分析时,大多采用功率谱分析,在提取调制谱时,效果不太理想,存在对噪声抑制能力差、提取后的谐波特征不明显等缺点。论文提出了一种基于EMD分解和1(1/2)维谱分析的提取方法,首先对目标辐射噪声信号进行EMD(Empirical Mode Decomposition)分解得到原始信号的若干个固有模态分量(Intrinsic Mode Function,IMF),其次对这些IMF分量进行解调,然后对其中某几个分量作1(1/2)谱分析得到舰船轴频的基频及谐波信息。仿真和对实测数据分析的结果表明,该方法可以有效提取噪声的特征信息,具有良好的应用前景。 The modulating information of ship radiation noise contains a lot of information about the characteristics of theship,which is used as the basis for the recognition and judgment of the sonar,and thus the type and speed of the target are identi-fied. When traditional DEMON(Detection of Envelope Modulation On Noise)spectrum analysis is performed,power spectrum anal-ysis is mostly used. When the modulation spectrum is extracted,the effect is not ideal,and there are disadvantages such as poornoise suppression capability and inconspicuous harmonic characteristics after extraction. This paper proposes an extraction methodbased on EMD decomposition and 1(1/2)-dimensional spectrum analysis. First,EMD(Empirical Mode Decomposition)decomposi-tion is used so as to obtain several intrinsic modal components of the original signal(Intrinsic Mode Function,IMF)of the targetnoise signal,secondly these IMF components are demodulated,and then perform 1(1/2)spectral analysis on some of them is per-formed to obtain the fundamental frequency and harmonic information of the ship's shaft frequency. The results of simulation andanalysis of measured data show that this method can effectively extract the characteristic information of noise and has a good applica-tion prospect.
作者 许劲峰 郑威 XU Jinfeng;ZHENG Wei(Jiangsu University of Science and Technogly,Zhenjiang 212003)
机构地区 江苏科技大学
出处 《舰船电子工程》 2018年第10期197-203,共7页 Ship Electronic Engineering
基金 江苏省自然科学基金(编号:BK20160565) 江苏省高校自然科学研究项目(编号:15KJB310003)资助
关键词 舰船噪声 EMD 1(1/2)维谱 特征提取 ship noise EMD 1 (1/2) Dspectrum feature extraction
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