基于时频特征的抹香鲸Click与传统声呐信号的分类方法  

Method for Classifying Sperm Whale Clicks and Traditional Sonar Signals Based on Time-Frequency Features

作　　者：卜令冉 华波 蒋佳佳[1] 颜晗 段发阶[1] 王宪全[1] 李春月 孙中波 Bu Lingran;Hua Bo;Jiang Jiajia;Yan Han;Duan Fajie;Wang Xianquan;Li Chunyue;Sun Zhongbo(State Key Lab of Precision Measuring Technology&Instruments,Tianjin University,Tianjin,300072,China;Systems Engineering Research Institute,China State Shipbuilding Corporation(CSSC),Beijing,100036,China)

机构地区：[1]天津大学精密测试技术及仪器国家重点实验室,天津300072 [2]中国船舶工业系统工程研究院,北京100036

出　　处：《数据采集与处理》2019年第5期844-853,共10页Journal of Data Acquisition and Processing

基　　金：天津市自然科学基金(17JCQNJC01100)资助项目;国家重点研发计划(2017YFF0204800)资助项目;国家自然科学基金(61501319,51775377,61505140)资助项目;光电信息与仪器北京市工程研究中心开放课题(GD2015007)资助项目;微光机电系统技术教育部重点实验室（天津大学）开放基金(MOMST2015-7)资助项目;中国科协“青年人才托举工程”(2016QNRC001)资助项目

摘　　要：正确识别与分类鲸类发出的叫声脉冲信号与主动声呐或通信信号,对提高海洋被动声学监测以及水下声呐探测或水下声学通信系统的稳定性和可靠性具有十分重要的作用。本文选取鲸声中具有代表性的Click信号和3类具有代表性的传统声呐信号作为研究对象,提出了一种基于时频特征的抹香鲸Click与传统声呐信号的分类方法。首先,利用滤波、小波去噪和端点检测方法实现鲸声去噪及信号自动摘取;然后,基于4类信号的短时傅里叶变换时频图,对信号时频轮廓进行多项式拟合,并提取多项式的系数作为信号时频特征;最后,分别使用反向传播(Back propagation,BP)神经网络和支持向量机对4类信号进行分类与识别。分类结果验证了所提算法和方法的有效性。Correctly identifying and classifying pulse signals of whales and active sonar or communication signals are very important for improving the stability and reliability of the marine passive acoustic monitoring and underwater sonar or underwater acoustic communication systems.In this paper,the representative Click signals of whales and three kinds of traditional sonar signals are selected as research objects,and a method for classifying sperm whale clicks and traditional sonar signals based on time-frequency features is proposed.Firstly,the denoising and automatic signal extraction of whale clicks are realized by using filtering,wavelet denoising and endpoint detection methods.Then,based on the short-time Fourier transform of the four types of signals,polynomials are used to fit the signal time-frequency contours,and the coefficients of the fitted polynomial are extracted as the time-frequency features of signals.Finally,the four types of signals are classified and identified using back propagation(BP)neural network and support vector machine respectively.The classification results verify the effectiveness of the proposed algorithm and method.

关 键 词：抹香鲸 声呐 短时傅里叶变换 BP神经网络 支持向量机 

分 类 号：TN919.5[电子电信—通信与信息系统]