基于小波分析的提取信号瞬时特征的研究
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摘要
在实际问题中，经常会遇到各种调制类型不明的信号。正确认识其调制类型，识别信号本身的一些基本特征，是信号处理的首要问题。本文就是利用小波变换可以识别突变信号的特点，提出了小波脊线的想法，并利用其去提取信号的瞬时幅度、瞬时频率和瞬时相位，以达到识别信号的目的。本文还给出了提取小波脊线的一种迭代算法，并对其初始值的选取进行了改进，使其计算误差尽可能的小，以达到识别信号的目的。此外，本文还给出了其他五种提取小波脊线的数值计算方法。并对以上六种算法，以计算所有采样点的小波脊线所用的时间作为依据，进行了对比，表明迭代算法也是一种行之有效的方法。
In practice,we are often up against some signals that its modulated type is not clear. It is the chiefly problem of signal disposal to recognize its modulated type and its essential characters rightly .In this paper, using the characteristic of wave-transform to recognize the abrupt signals, we put forward an idea of wavelet-transform-curve in order to extract the instantaneous-extent,instantaneous-frequency,instantaneous-post-ure of signals.accordingly we can recognize the signals .In this paper we also bring forward an iterative algorithm to gain the wavelet-transform.At the same time I achieved less error result by means of improving the initial-valued.The morever ,In this paper,I also give five algorithms in numerical computation that can extract the wavelet-ridge-curve .For all these algorithms ,being based on the time that calculated the all sampling-points of wavelet-ridge-curve, I made a contrast.As a result,it indicates that this iterative algolrithm is a effective method.
In practice,we are often up against some signals that its modulated type is not clear. It is the chiefly problem of signal disposal to recognize its modulated type and its essential characters rightly .In this paper, using the characteristic of wave-transform to recognize the abrupt signals, we put forward an idea of wavelet-transform-curve in order to extract the instantaneous-extent,instantaneous-frequency,instantaneous-post-ure of signals.accordingly we can recognize the signals .In this paper we also bring forward an iterative algorithm to gain the wavelet-transform.At the same time I achieved less error result by means of improving the initial-valued.The morever ,In this paper,I also give five algorithms in numerical computation that can extract the wavelet-ridge-curve .For all these algorithms ,being based on the time that calculated the all sampling-points of wavelet-ridge-curve, I made a contrast.As a result,it indicates that this iterative algolrithm is a effective method.
引文
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2．沈剑华：计算数学基础，同济大学出版社，1988
3．程正兴：小波分析算法与应用，西安交通大学出版社，1998
4．胡昌华等：基于MATLAB的系统分析与设计．小波分析，西安电子科技大学出版社，1999
5．汪润生、周师熊等：数据通信工程，人民邮电出版社，1987
6．邓必鑫：信号分析基础，北京理工大学出版社，1993
7．曹志刚、钱亚生：现代调制技术，清华大学出版社，1992
8．秦前清、杨宗凯：实用小波分析，西安电子科技大学出版社，1994
9．崔锦泰：小波分析导论，西安交通大学出版社，1995
10．赵建平、黄建国：信号瞬时特征的小波分析，第五届全国信号处理学术会议论文集(CCSP-94)，ppl85-187，1994
11．徐长发，李国宽：实用小波方法，华中科技大学出版社，1999
12．梁泰基：统计无线电理论，国防科学大学出版社，1988
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报 Vol．19，No．6 P20-25 1998．6
17．程俊 张璞：小波变换用于信号突变的检测，通信学报 Vol．16，No．3 P96-104 1995．3
18．刘九芳 黄达人：一个优化小波块量化矩阵的算法，电子学报 Vol．29，NO．5 P607-610 2001．5
19．李建平主编：小波分析与信号处理．理论应用及软件实现，重庆出版社 1997．12
20．郑权：具有参数的不带有导数的平方收敛的迭代法，计算数学 Vol．25，No．1 P107-112
21．吴新元：对牛顿迭代法的一个重要修改，应用数学和力学 Vol．20，No．8 P863-866
22．V K．Ingle,J G．Proakis：数字信号处理及其MATLAB实现，电子工业出版社 1998
23．王首勇：应用最优小波包变换的特征提取方法，电子学报 Vol．31．No．7 P1035-1038
24. R R Coifman,M V W Hauser.Enthopy based algorithms for best basis selextion [J] .IEEE Trans, 1992,IT-38(3);P713-718
25. R Samkays,J H L Hansen.High Resolution speech feature parameterization for monophone-based stressed speech recognition [J] ,IEEE SP Letters.2000,7(7);P 182-185
26．张贤达、保铮：非平稳信号分析与处理，国防工业出版社，1998．9
27．西安交通大学高等数学教研室：复变函数，人民教育出版社，1981
28．朱华、黄辉宁等：随机信号分析，北京理工大学出版社，1990
29．徐秉铮、欧阳景正：信号分析与相关技术，科学出版社，1981
30. Grossmann A and Morlet J:Decomposition of Hardy function into square
integrable wavelets of constract shape.SIAM J Math Anal 1984,15 PP723-736
31. Malat S:A theory of multiresolution signal decomposition:the wavelet representation.IEEE Trans Pattern Anal Machine Intell. 1989,11,PP674-693
32. Mallat S and Zhong S:Characterization of signals from multiscale edge. IEEE Trans Part Anal and Math Intell.1992,14 PP710-732
33. I.Danbechies:Time-Frequency Localization Operators-A Geometric Phase Space Approach, IEEE Trans.Inform.Theory, vol 34,No.4, pp605-612,1988
2．沈剑华：计算数学基础，同济大学出版社，1988
3．程正兴：小波分析算法与应用，西安交通大学出版社，1998
4．胡昌华等：基于MATLAB的系统分析与设计．小波分析，西安电子科技大学出版社，1999
5．汪润生、周师熊等：数据通信工程，人民邮电出版社，1987
6．邓必鑫：信号分析基础，北京理工大学出版社，1993
7．曹志刚、钱亚生：现代调制技术，清华大学出版社，1992
8．秦前清、杨宗凯：实用小波分析，西安电子科技大学出版社，1994
9．崔锦泰：小波分析导论，西安交通大学出版社，1995
10．赵建平、黄建国：信号瞬时特征的小波分析，第五届全国信号处理学术会议论文集(CCSP-94)，ppl85-187，1994
11．徐长发，李国宽：实用小波方法，华中科技大学出版社，1999
12．梁泰基：统计无线电理论，国防科学大学出版社，1988
13. I.Daubechies:The Wavelet Transform,Time-Frequency Localization and Signal Analysis,IEEE Trans.Inform Theory, vol.36 No.5. pp961-1005,1990
14. A.V.O and R.W.S:DIGITAL SIGNAL PROCESSING,prentice-Hall,Inc., 1975
15. N.Delprat,B.Escudie:Asymptotic Wavelet and Gabor Analysis Extraction Instantaneous Frequencies,IEEE Trans.Inform. Theory, vol.38,No.2,pp644-664,1992
16．马鸿飞、樊昌信：基于M-频带小波变换的宽带语音编码算法，通信学
报 Vol．19，No．6 P20-25 1998．6
17．程俊 张璞：小波变换用于信号突变的检测，通信学报 Vol．16，No．3 P96-104 1995．3
18．刘九芳 黄达人：一个优化小波块量化矩阵的算法，电子学报 Vol．29，NO．5 P607-610 2001．5
19．李建平主编：小波分析与信号处理．理论应用及软件实现，重庆出版社 1997．12
20．郑权：具有参数的不带有导数的平方收敛的迭代法，计算数学 Vol．25，No．1 P107-112
21．吴新元：对牛顿迭代法的一个重要修改，应用数学和力学 Vol．20，No．8 P863-866
22．V K．Ingle,J G．Proakis：数字信号处理及其MATLAB实现，电子工业出版社 1998
23．王首勇：应用最优小波包变换的特征提取方法，电子学报 Vol．31．No．7 P1035-1038
24. R R Coifman,M V W Hauser.Enthopy based algorithms for best basis selextion [J] .IEEE Trans, 1992,IT-38(3);P713-718
25. R Samkays,J H L Hansen.High Resolution speech feature parameterization for monophone-based stressed speech recognition [J] ,IEEE SP Letters.2000,7(7);P 182-185
26．张贤达、保铮：非平稳信号分析与处理，国防工业出版社，1998．9
27．西安交通大学高等数学教研室：复变函数，人民教育出版社，1981
28．朱华、黄辉宁等：随机信号分析，北京理工大学出版社，1990
29．徐秉铮、欧阳景正：信号分析与相关技术，科学出版社，1981
30. Grossmann A and Morlet J:Decomposition of Hardy function into square
integrable wavelets of constract shape.SIAM J Math Anal 1984,15 PP723-736
31. Malat S:A theory of multiresolution signal decomposition:the wavelet representation.IEEE Trans Pattern Anal Machine Intell. 1989,11,PP674-693
32. Mallat S and Zhong S:Characterization of signals from multiscale edge. IEEE Trans Part Anal and Math Intell.1992,14 PP710-732
33. I.Danbechies:Time-Frequency Localization Operators-A Geometric Phase Space Approach, IEEE Trans.Inform.Theory, vol 34,No.4, pp605-612,1988