基于经验模态分解的物体入水声检测及测向研究
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摘要
物体入水声是一种瞬态信号,其波形由击水声和若干气泡脉动组成。传统的矢量信号处理方法对此类瞬态信号的检测和测向会出现困难,尤其是在信噪比较低时检测不到入水声信号。经验模态分解是一种突出信号局部瞬态特性的非线性分析方法,将矢量传感器接收的声压、振速信息分解为不同的固有模态函数,利用文中提出的模态声强器的方位估计算法,可以实现瞬态信号的检测和测向。湖试和海试结果表明该方法能把本地干扰和入水声分解到不同的模态函数中,利用模态声强器可以在本地强干扰下有效检测到入水声信号出现的时间,并可以实现测向。
The splash sound of target's water entry is an instantaneous signal~ consisting of impact component and bubble components.It is difficult to detect and estimate the azimuth of this signal by traditional azimuth estimation methods using a single vector sensor,especially when the SNR is low.Empirical mode decomposition is a nonlinear analysis method which can emphasize signals' instantaneous characteristic.In this paper,the pressure and particle velocities are decomposed into different intrinsic mode functions(IMF),and the MAIA(mode acoustic intensity averager) method can realize the instantaneous signals' detection and azimuth estimation.The lake experiment and sea trial results show that this method can decompose water-entry sound and ambient interference into different IMFs,so as to detect the starting time of water-entry sound signal and estimate azimuth effectively.
The splash sound of target's water entry is an instantaneous signal~ consisting of impact component and bubble components.It is difficult to detect and estimate the azimuth of this signal by traditional azimuth estimation methods using a single vector sensor,especially when the SNR is low.Empirical mode decomposition is a nonlinear analysis method which can emphasize signals' instantaneous characteristic.In this paper,the pressure and particle velocities are decomposed into different intrinsic mode functions(IMF),and the MAIA(mode acoustic intensity averager) method can realize the instantaneous signals' detection and azimuth estimation.The lake experiment and sea trial results show that this method can decompose water-entry sound and ambient interference into different IMFs,so as to detect the starting time of water-entry sound signal and estimate azimuth effectively.
引文
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5张宾，孙长瑜，孙贵青．基于经验模式分解的拖曳式声呐拖船噪声抵消研究．应用声学，2007；26(2)：68—73
6王逸林，蔡平，许丹丹．应用希尔伯特-黄变换的单矢量传感器多目标分辨研究．声学技术，2006；25(4)：376—380
7钟佑明，秦树人，汤宝平．Hilbert—Huang变换中的理论研究．振动与冲击，2002；21(4)：13-17
8 Yang J N,Lei Y,Lin S,Huang N.Hilbert-Huang based approach for structural damage detection.Journal of en- gineering mechanics,2004;130(1):85-95
9钟佑明，秦树人．Hilbert—Huang变换中的统一理论依据研究．振动与冲击，2006；25(3)：40-43
10杨培杰，印兴耀，张广智．希尔比特-黄变换地震信号时频分析与属性提取．地球物理学进展，2007；22(5)：1585-1589
11杨宁，于德介，程军圣．基于EMD与神经网络的滚动轴承故障诊断方法．振动与冲击，2005；24(1)：85-88
12惠俊英，刘宏，余华兵等．声压振速联合信号处理及物理基础初探．声学学报，2000；25(4)：303-307
13王德俊．矢量声场与矢量信号处理理论研究．博士学位论文，哈尔滨工程大学，2004
14汤渭霖．水下噪声学原理．哈尔滨：哈尔滨船舶工程学院出版社，1984：127-132
15鲍筱玲，汤渭霖．击水声特性研究．哈尔滨船舶工程学院学报，1981(1)：27—39
2姚直象，惠俊英，王德俊等．用单个矢量水听器基于DEMON线谱的双目标分辨．应用声学，2005；24(3)：171—176
3 Oonincx P J,Hermand J P.Empirical mode decomposition of ocean acoustic data with constraint on the frequency range.Proceeding of the seventh European conference on acoustics,2004
4 Huang N E,Shen Z,Long S R et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis.Proc R Soc,1998; A454:903-995
5张宾，孙长瑜，孙贵青．基于经验模式分解的拖曳式声呐拖船噪声抵消研究．应用声学，2007；26(2)：68—73
6王逸林，蔡平，许丹丹．应用希尔伯特-黄变换的单矢量传感器多目标分辨研究．声学技术，2006；25(4)：376—380
7钟佑明，秦树人，汤宝平．Hilbert—Huang变换中的理论研究．振动与冲击，2002；21(4)：13-17
8 Yang J N,Lei Y,Lin S,Huang N.Hilbert-Huang based approach for structural damage detection.Journal of en- gineering mechanics,2004;130(1):85-95
9钟佑明，秦树人．Hilbert—Huang变换中的统一理论依据研究．振动与冲击，2006；25(3)：40-43
10杨培杰，印兴耀，张广智．希尔比特-黄变换地震信号时频分析与属性提取．地球物理学进展，2007；22(5)：1585-1589
11杨宁，于德介，程军圣．基于EMD与神经网络的滚动轴承故障诊断方法．振动与冲击，2005；24(1)：85-88
12惠俊英，刘宏，余华兵等．声压振速联合信号处理及物理基础初探．声学学报，2000；25(4)：303-307
13王德俊．矢量声场与矢量信号处理理论研究．博士学位论文，哈尔滨工程大学，2004
14汤渭霖．水下噪声学原理．哈尔滨：哈尔滨船舶工程学院出版社，1984：127-132
15鲍筱玲，汤渭霖．击水声特性研究．哈尔滨船舶工程学院学报，1981(1)：27—39
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