基于广义S变换的信号提取与抑噪
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摘要
S变换结合了短时傅里叶变换和小波变换的优点,是非平稳信号时频分析的有力工具。为了提高S变换在应用中的实用性和灵活性,该文将S变换改造成时频分辨率可调的广义S变换。广义S变换具有多分辨率特性,利用其高质量的时频分布,在时频域中设计了两种时频滤波器。前者用来实现非平稳信号中特定信号分量的提取,也可滤除处于特定时频平面中的噪声;后者直接应用广义S变换的时频谱实现,用于含高斯白噪声信号的滤波,达到了突出有效信号和压制噪声的效果。几种信号模型的仿真试验显示了它们在信号提取和抑制噪声中应用的有效性。
S transform(ST) combines the advantages of short time Fourier transform(STFT) and wavelet transform(WT).It is a powerful means for time-frequency analysis of non-stationary signal.In order to improve the practicability and adaptability of S transform in actual application,a generalized S transform(GST) whose time-frequency resolution can be adjustable is achieved by altering normal S transform.GST provides multiresolution analysis.Two time-frequency filters based on its excellent time-frequency distribution are presented.The first one can extract specific signal components and filter noise on the particular time-frequency plane.Another applied in filtering signal containing Gaussian white noise is computed directly by utilizing time-frequency spectrum of GST,which can enhance non-stationary signal and suppress noise.The simulation demonstrates that two filters based on GST are effective in signal extracting and denoising.
S transform(ST) combines the advantages of short time Fourier transform(STFT) and wavelet transform(WT).It is a powerful means for time-frequency analysis of non-stationary signal.In order to improve the practicability and adaptability of S transform in actual application,a generalized S transform(GST) whose time-frequency resolution can be adjustable is achieved by altering normal S transform.GST provides multiresolution analysis.Two time-frequency filters based on its excellent time-frequency distribution are presented.The first one can extract specific signal components and filter noise on the particular time-frequency plane.Another applied in filtering signal containing Gaussian white noise is computed directly by utilizing time-frequency spectrum of GST,which can enhance non-stationary signal and suppress noise.The simulation demonstrates that two filters based on GST are effective in signal extracting and denoising.
引文
[1]张贤达,保铮.非平稳信号分析与处理[M].北京:国防工业出版社,2001.
[2]陆根源,陈孝桢.信号检测与参数估计[M].北京:科学出版社,2004.
[3]STOCKWELL R G,MANSINHA L,LOWE R P.Localization of the complex spectrum:the S trans-form[J].IEEE Transactions on Signal Processing,1996,44(4):998-1001.
[4]PINNEGAR C R,MANSINHA L.The S-transformwith windows of arbitrary and varying shape[J].Geophysics,2003,68(1):381-385.
[5]PINNEGAR C R,MANSINHA L.Time-local spec-tral analysis for non-stationary time series:the S-transform for noisy signals[J].Fluctuation and NoiseLetters,2003,3(3):357-364.
[6]PINNEGAR C R,EATON D W.Application of theS transform to prestack noise attention filtering[J].Journal of Geophysical Research,2003,108(B9):2422-2431.
[7]高静怀,陈文超,李幼铭,等.广义S变换与薄互层地震响应分析[J].地球物理学报,2003,46(4):526-532.
[8]陈学华,贺振华.改进的S变换及在地震信号处理中的应用[J].数据采集与处理,2005,20(4):449-453.
[2]陆根源,陈孝桢.信号检测与参数估计[M].北京:科学出版社,2004.
[3]STOCKWELL R G,MANSINHA L,LOWE R P.Localization of the complex spectrum:the S trans-form[J].IEEE Transactions on Signal Processing,1996,44(4):998-1001.
[4]PINNEGAR C R,MANSINHA L.The S-transformwith windows of arbitrary and varying shape[J].Geophysics,2003,68(1):381-385.
[5]PINNEGAR C R,MANSINHA L.Time-local spec-tral analysis for non-stationary time series:the S-transform for noisy signals[J].Fluctuation and NoiseLetters,2003,3(3):357-364.
[6]PINNEGAR C R,EATON D W.Application of theS transform to prestack noise attention filtering[J].Journal of Geophysical Research,2003,108(B9):2422-2431.
[7]高静怀,陈文超,李幼铭,等.广义S变换与薄互层地震响应分析[J].地球物理学报,2003,46(4):526-532.
[8]陈学华,贺振华.改进的S变换及在地震信号处理中的应用[J].数据采集与处理,2005,20(4):449-453.
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