船舶地声信号相干分析方法
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摘要
低频声波与海底交互耦合使海底成为有效的地声传播路径,利用船舶地声信号可以实现目标的探测定位。传统傅氏变换因时间序列平稳性假定的局限性已不适合于非平稳地声信号的分析,小波变换将时间序列扩展至时频空间,使小波变换具有良好的局域性和逼近性。借鉴傅氏变换的形式,定义小波谱和小波相干系数并对船舶地声信号进行分析,在时频域直观地展现了信号间的关系。数据分析表明:水声信号和地声信号在2.5-50Hz频带内是相干的,是同一声源发出的不同路径传播的相干信号,相应的SCR(信号耦合比)值随频率增加先减少后略微增加,同时体现出小波谱和小波相干系数是一种有效的分析信号相干性的方法。
Geoacoustic signal can effectively propagate along the seabed for low frequency acoustic wave coupling to the bottom. Ship geoacoustic signal can be used as a method of target detection and localization. Ship geoacoustic signal is a non-stationary random process, so the traditional approaches based on Fourier transform are improper. Wavelet transform is a powerful tool, which can make clear the local details of signal in time-frequency field. In this paper, wavelet transform can be interpreted as an extension of the Fourier transformation. By transferring a concept of Fourier analysis, wavelet spectrum and wavelet coherency can be defined. Data analysis illustrate that two signals (geoacoustic and hydroacoustic) are coherent in the frequency band of 2.5-50 Hz, and are derived from the same source. The tendency of SCR shows a decrease with the frequency, but a little increased latter. It is proved that wavelet spectrum and wavelet coherency are adapted for coherency analysis and good at time frequency positioning.
Geoacoustic signal can effectively propagate along the seabed for low frequency acoustic wave coupling to the bottom. Ship geoacoustic signal can be used as a method of target detection and localization. Ship geoacoustic signal is a non-stationary random process, so the traditional approaches based on Fourier transform are improper. Wavelet transform is a powerful tool, which can make clear the local details of signal in time-frequency field. In this paper, wavelet transform can be interpreted as an extension of the Fourier transformation. By transferring a concept of Fourier analysis, wavelet spectrum and wavelet coherency can be defined. Data analysis illustrate that two signals (geoacoustic and hydroacoustic) are coherent in the frequency band of 2.5-50 Hz, and are derived from the same source. The tendency of SCR shows a decrease with the frequency, but a little increased latter. It is proved that wavelet spectrum and wavelet coherency are adapted for coherency analysis and good at time frequency positioning.
引文
[1]陈云飞,吕俊军.航行舰船地震波及其在水中目标探测中的应用[J].舰船科学技术,2005,27(3):62-66.
[2]李跃华,李兴国.小波谱估计用于雷达目标成像和识别[J].红外与毫米波学报,1999,18(4):283-288.
[3]Andrew W T,Donald P P.Wavelet methods for times series analysis[M].Cambridge:Cambridge University Press,2000.
[4]胡广书.现代信号处理教程[M].北京:清华大学出版社,2001:48-118.
[5]Torrence C,Compo G P.A practical guide to wavelet analysis[J].Bull.Am.Meteorol.Soc.,1998,79:61-78.
[6]Nason G,von Sachs R,Kroisandt G.Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum[J].J Roy.Stat.Soc.B,2000,62:271-292.
[7]Percival D,Walden A.Wavelet methods for time series analysis[M].Cambridge:Cambridge University Press,2000.
[8]Grinsted A,Moore J C,Jevrejeva S.Application of the cross wavelet transform and wavelet coherence to geophysical time series[J].Nonlinear Processes in Geophysics,2004(11):561-566.
[2]李跃华,李兴国.小波谱估计用于雷达目标成像和识别[J].红外与毫米波学报,1999,18(4):283-288.
[3]Andrew W T,Donald P P.Wavelet methods for times series analysis[M].Cambridge:Cambridge University Press,2000.
[4]胡广书.现代信号处理教程[M].北京:清华大学出版社,2001:48-118.
[5]Torrence C,Compo G P.A practical guide to wavelet analysis[J].Bull.Am.Meteorol.Soc.,1998,79:61-78.
[6]Nason G,von Sachs R,Kroisandt G.Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum[J].J Roy.Stat.Soc.B,2000,62:271-292.
[7]Percival D,Walden A.Wavelet methods for time series analysis[M].Cambridge:Cambridge University Press,2000.
[8]Grinsted A,Moore J C,Jevrejeva S.Application of the cross wavelet transform and wavelet coherence to geophysical time series[J].Nonlinear Processes in Geophysics,2004(11):561-566.
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