基于多尺度排列熵的舰船辐射噪声复杂度特征提取研究
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摘要
针对复杂海洋环境中舰船辐射噪声的特征提取问题,提出了一种基于多尺度排列熵的舰船辐射噪声复杂度特征提取方法。分别利用基于EEMD的最强固有模态中心频率法、高低频能量差法和基于复杂度的排列熵与多尺度排列熵提取了五种不同类别、一定样本数量的舰船辐射噪声特征,并将四种特征提取方法所提取的舰船特征分别输入概率神经网络进行分类识别。研究发现,多尺度排列熵是一种一致性好、稳定性强的非线性特征参数,能够从多个维度描述信号的复杂度。实验结果表明,多尺度排列熵特征具有很好的可分性,以多尺度排列熵为特征进行舰船分类识别,识别率显著高于其他舰船辐射噪声特征提取算法。
In order to solve the problem of feature extraction of ship radiated signals in complex ocean environment, a multi-scale permutation entropy method based the complexity feature extraction method for ship radiated signals was proposed. Firstly, the center frequency of intrinsic mode function with the highest energy, the energy difference between high and low frequency, permutation entropy and multi-scale permutation entropy were respectively used to extracted features of five types of ship radiated signals. Then the extracted ship features by four kinds of methods were respectively sent into a probability neural network for identification. The study discovers that multi-scale permutation entropy is a powerful nonlinear characteristic because it has good consistency and stability and is able to describe a signal over multiple scales. The results indicate that the multi-scale permutation entropy method has a good separability. The identification accuracy is obvious higher than other ship radiated noise feature extraction methods when using multi-scale permutation entropy as the feature.
In order to solve the problem of feature extraction of ship radiated signals in complex ocean environment, a multi-scale permutation entropy method based the complexity feature extraction method for ship radiated signals was proposed. Firstly, the center frequency of intrinsic mode function with the highest energy, the energy difference between high and low frequency, permutation entropy and multi-scale permutation entropy were respectively used to extracted features of five types of ship radiated signals. Then the extracted ship features by four kinds of methods were respectively sent into a probability neural network for identification. The study discovers that multi-scale permutation entropy is a powerful nonlinear characteristic because it has good consistency and stability and is able to describe a signal over multiple scales. The results indicate that the multi-scale permutation entropy method has a good separability. The identification accuracy is obvious higher than other ship radiated noise feature extraction methods when using multi-scale permutation entropy as the feature.
引文
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[18] 郑小霞,周国旺,任浩瀚,等.基于变分模态分解和排列熵的滚动轴承故障诊断[J].振动与冲击,2017,36(22):22-28.ZHENG Xiaoxia,ZHOU Guowang,REN Haohan,et al.Rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy[J].Journal of Vibration and Shock,2017,36(22):22-28.
[19] 丁闯,张兵志,冯辅周,等.局部均值分解和排列熵在行星齿轮箱故障诊断中的应用[J].振动与冲击,2017,36(17):55-60.DING Chuang,ZHANG Bingzhi,FENG Fuzhou,et al.Application of local mean decomposition and permutation entropy in fault diagnosis of planetary gearbox[J].Journal of Vibration and Shock,2017,36(17):55-60.
[20] 郝旺身,王洪明,董辛旻,等.基于全矢排列熵的齿轮故障特征提取方法研究[J].振动与冲击,2016,35(11):224-228.HAO Wangshen,WANG Hongming,DONG Xinmin,et al.Gear fault feature extraction based on full vector permutation entropy[J].Journal of Vibration and Shock,2016,35(11):224-228.
[21] SPECHT D F.Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification[J].IEEE Trans.Neural Netw,1990,1(1):111-121.
[22] 姚文坡,刘铁兵,戴加飞,等.脑电信号的多尺度排列熵分析[J].物理学报,2014,63(7):078704.YAO Wenpo,LIU Tiebing,DAI Jiafei,et al.Multiscale permutation entropy analysis of electroencephalogram[J].Acta Physica Sinica,2014,63(7):078704.
[23] LI Y,CHEN Z.Entropy based underwater acoustic signal detection[C]//International Bhurban Conference on Applied Sciences and Technology.IEEE,2017:656-660.
[2] 陈志光,李亚安,陈晓.基于Hilbert变换及间歇混沌的水声微弱信号检测方法研究[J].物理学报,2015,64(20):69-76.CHEN Zhiguang,LI Yaan,CHEN Xiao.Underwater acoustic weak signal detection based on Hilbert transform and intermittent chaos[J].Acta Physica Sinica,2015,64(20):69-76.
[3] 李亚安,徐德民,张效民.舰船噪声信号的混沌特性研究[J].西北工业大学学报,2001,19(2):266-269.LI Yaan,XU Demin,ZHANG Xiaomin.Study on chaotic characteristics of the signal of ship radiated noise[J].Journal of Northwestern Polytechnical University,2001,19(2):266-269.
[4] 吴国清,李靖,陈耀明,等.舰船噪声识别(Ⅰ)——总体框架、线谱分析和提取[J].声学学报,1998,23(5):394-400.WU Guoqing,LI Jing,CHEN Yaoming,et al.Ship radiated noise recognition (Ⅰ) the overall framework,analysis and extraction of line-spectrum[J].Acta Acustica,1998,23(5):394-400.
[5] 张岩.多元统计分析在舰船辐射噪声分类识别中的应用[D].北京:中国科学院声学研究所,2007.
[6] 章新华,王骥程,林良骥.基于小波变换的舰船辐射噪声特征提取[J].声学学报,1997,22(2):139-137.ZHANG Xinhua,WANG Jicheng,LIN Liangji.Feature extraction of ship radiated noise based on wavelet transform[J].Acta Acustica,1997,22(2):139-137.
[7] 杨宏,李亚安,李国辉.基于集合经验模态分解的舰船辐射噪声能量分析[J].振动与冲击,2015,34(16):55-59.YANG Hong,LI Yaan,LI Guohui.Energy analysis of ship radiated nosie based on ensemble empirical mode decomposition[J].Journal of Vibration and Shock,2015,34(16) :55-59.
[8] 李余兴,李亚安,陈晓.基于EEMD的舰船辐射噪声特征提取方法研究[J].振动与冲击,2017,36(5):114-119.LI Yuxing,LI Yaan,CHEN Xiao.Ships’ radiated noise feature extraction based on EEMD[J].Journal of Vibration and Shock,2017,36(5) :114-119.
[9] HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London.Series A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[10] WU Z H,HUANG N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[11] 高云超.希尔伯特-黄变换在水声信号处理中的应用研究[D].哈尔滨:哈尔滨工程大学,2009.
[12] 李秀坤,谢磊,秦宇.应用希尔伯特黄变换的水下目标特征提取[J].哈尔滨工程大学学报,2009,30(5):542-546.LI Xiukun,XIE Lei,QIN Yu.Underwater target featuer extraction using Hilbert-Huangtransform[J].Journal of Harbin Engineering University,2009,30(5):542-546.
[13] 刘深,张小蓟,牛奕龙,等.基于IMF能量谱的水声信号特征提取分类[J].计算机工程与应用,2014,50(3):203-206.LIU Shen,ZHANG Xiaoji,NIU Yilong,et al.Feature extraction and classification experiment of underwater acoustic signals based on energy spectrum of IMF[J].Computer Engineering and Applications,2014,50(3):203-206.
[14] 李琳,张永祥,明廷涛.EMD降噪的关联维数在齿轮故障诊断中的应用研究[J].振动与冲击,2009,28(4):145-148.LI Lin,ZHANG Yongxiang,MING Tingtao.Gear fault diagnosis based on correlation dimension pre-processed with EMD[J].Journal of Vibration and Shock,2009,28(4):145-148.
[15] RICHMAN J S,MOORMAN J R.Physiological time-series analysis using approximate entropy and sample entropy[J].American Journal of Physiology Heart & Circulatory Physiology,2000,278(6):2039-2049.
[16] COSTA M,GOLDBERGER A L,PENG C K.Multiscale entropy analysis of complex physiologic time series.[J].Physical Review Letters,2002,89(6):068102.
[17] BANDT C,POMPE B.Permutation entropy:a natural complexity measure for time series[J].Physical Review Letters,2002,88(17):174102.
[18] 郑小霞,周国旺,任浩瀚,等.基于变分模态分解和排列熵的滚动轴承故障诊断[J].振动与冲击,2017,36(22):22-28.ZHENG Xiaoxia,ZHOU Guowang,REN Haohan,et al.Rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy[J].Journal of Vibration and Shock,2017,36(22):22-28.
[19] 丁闯,张兵志,冯辅周,等.局部均值分解和排列熵在行星齿轮箱故障诊断中的应用[J].振动与冲击,2017,36(17):55-60.DING Chuang,ZHANG Bingzhi,FENG Fuzhou,et al.Application of local mean decomposition and permutation entropy in fault diagnosis of planetary gearbox[J].Journal of Vibration and Shock,2017,36(17):55-60.
[20] 郝旺身,王洪明,董辛旻,等.基于全矢排列熵的齿轮故障特征提取方法研究[J].振动与冲击,2016,35(11):224-228.HAO Wangshen,WANG Hongming,DONG Xinmin,et al.Gear fault feature extraction based on full vector permutation entropy[J].Journal of Vibration and Shock,2016,35(11):224-228.
[21] SPECHT D F.Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification[J].IEEE Trans.Neural Netw,1990,1(1):111-121.
[22] 姚文坡,刘铁兵,戴加飞,等.脑电信号的多尺度排列熵分析[J].物理学报,2014,63(7):078704.YAO Wenpo,LIU Tiebing,DAI Jiafei,et al.Multiscale permutation entropy analysis of electroencephalogram[J].Acta Physica Sinica,2014,63(7):078704.
[23] LI Y,CHEN Z.Entropy based underwater acoustic signal detection[C]//International Bhurban Conference on Applied Sciences and Technology.IEEE,2017:656-660.
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