倾角传感器信号的小波分析与处理
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摘要
小波理论经过十多年的发展,取得了许多非常重要的成果,分析方法日益丰富,应用范围日趋广泛。小波分析成功与否,必须视实际问题而定。如何根据信号的特点和信号处理的需要选取最优小波类型和处理方法,一直是小波理论应用研究领域的重要问题之一。
首先,本文研究了小波的最新理论,在对小波信号处理方法进行系统和深入的讨论后,给出了小波分析在信号消噪中的新方法,并进行了实验检验。另一方面,现在的倾角传感器产品虽然精度可以做到很高,但是却很容易受到外界的干扰。为此,本文研究了小波分析理论在倾角传感器信号处理中的应用。工作包括了对倾角传感器信号的消噪处理以及倾角传感器不同激励下信号特征的区分。其中消噪处理利用了一维小波变换,提出了一种新的阈值函数,同时对于方差未知的信号则提出了一种将GCV规则和遗传算法结合的新方法,并验证了方法的优秀性。针对倾角传感器动态测量中信号夹杂加速度影响成份的问题,本文讨论了用小波变换方法进行区分的可能性。
After more than 10 years of development, wavelet theory has made many very important achievements, analysis methods becoming more and more plentiful ,application range being increasingly broad. The success or failure of Wavelet analysis depends on particular practical issues. How to select the optimal wavelet type and approach, according to the characteristics of signal and processing need, has been one of the important issues in the field of wavelet theory and application research.
First of all, this paper studies the latest theory of wavelet, after systematic and deep discussion of the wavelet signal processing methods, it provides a new denoising methods and gives experimental test results. On the other hand, though very high accuracy can be done now, but angle sensor products are still very vulnerable to outside interference. Therefore, this article focuses on the applications of wavelet analysis in angle sensor signal processing, including the angle sensor signal denoising and signal identification when the sensor is inspired by different signals. During denoising processing research which depends on the one-dimensional wavelet transform, this paper gives a new function for signals whose noise variance is already known. As for signals with unknown variance, a GCV rule methods combining genetic algorithm is proposed. As signal of tilt sensor would be mixed with acceleration influence during dynamic measurement, possibility of distinguish acceleration influence with wavelet is discussed.
首先,本文研究了小波的最新理论,在对小波信号处理方法进行系统和深入的讨论后,给出了小波分析在信号消噪中的新方法,并进行了实验检验。另一方面,现在的倾角传感器产品虽然精度可以做到很高,但是却很容易受到外界的干扰。为此,本文研究了小波分析理论在倾角传感器信号处理中的应用。工作包括了对倾角传感器信号的消噪处理以及倾角传感器不同激励下信号特征的区分。其中消噪处理利用了一维小波变换,提出了一种新的阈值函数,同时对于方差未知的信号则提出了一种将GCV规则和遗传算法结合的新方法,并验证了方法的优秀性。针对倾角传感器动态测量中信号夹杂加速度影响成份的问题,本文讨论了用小波变换方法进行区分的可能性。
After more than 10 years of development, wavelet theory has made many very important achievements, analysis methods becoming more and more plentiful ,application range being increasingly broad. The success or failure of Wavelet analysis depends on particular practical issues. How to select the optimal wavelet type and approach, according to the characteristics of signal and processing need, has been one of the important issues in the field of wavelet theory and application research.
First of all, this paper studies the latest theory of wavelet, after systematic and deep discussion of the wavelet signal processing methods, it provides a new denoising methods and gives experimental test results. On the other hand, though very high accuracy can be done now, but angle sensor products are still very vulnerable to outside interference. Therefore, this article focuses on the applications of wavelet analysis in angle sensor signal processing, including the angle sensor signal denoising and signal identification when the sensor is inspired by different signals. During denoising processing research which depends on the one-dimensional wavelet transform, this paper gives a new function for signals whose noise variance is already known. As for signals with unknown variance, a GCV rule methods combining genetic algorithm is proposed. As signal of tilt sensor would be mixed with acceleration influence during dynamic measurement, possibility of distinguish acceleration influence with wavelet is discussed.
引文
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[3].王济,胡晓.MATLAB在振动信号处理中的应用.北京:中国水利水电出版社,知识产权出版社.2006.
[4].王小平,曹立明.遗传算法—理论、应用与软件实现.西安:西安交通大学出版社.2003.
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[6].王裕鹏,赵龙庆,张华伟.基于小波改进阈值消噪的缸盖振动信号分析.柴油机,2007,VOL.29(NO.5):37-39.
[7].韦力强,谭阳红,贺迅宇,龙春阳.基于新阈值函数的小波阈去噪研究.计算技术与自动化,2006,VOL.25(NO.4):189-191.
[8].田小芳,陆起涌,熊超.基于加速度传感器的倾角仪设计.传感技术学报,2006,VOL.19(NO.2):361-363.
[9].冯轲.基于GCV规则的小波阈值去噪算法研究.科学之友,2007,10月刊:59-60.
[10].付炜,彭光剑.基于小波阈值去噪的改进方法.电子测量技术,2006,VOL.29(NO.6):46-48.
[11].阮沈勇,王永利,桑群芳.MATLAB程序设计.北京:电子工业出版社.2004.
[12].朴林华,张伟,张金铎,张福学.加速度对气体摆式倾角传感器影响机理的探讨.传感器技术,2004,VOL.23(NO.7):4-7.
[13].刘波,聂明新,向俊涛.基于小波分析的阈值语音信号去噪研究.现代计算机,2007,NO.257:20-22.
[14].汤爱芳,祝连庆,董明利.小波多分辨分析在超声回波信号去噪中的应用.计算机仿真,2006,VOL.23(NO.11):318-321
[15].李大卫,王梦光.一种改进的混合遗传算法.信息与控制,1997,VOL26(NO.6):449-454.
[16].邹鲲,袁俊泉,龚享铱.MATLAB6.x信号处理.北京:清华大学出版社.2002.
[17].陈进军,温志渝,文方,王武.液态电极电容式倾角传感器.仪器仪表学报,2004,VOL.25(NO.4):219-220.
[18].陈秋莲,王成栋.基于MATLAB遗传工具箱的优化计算实现.现代电子技 术,2006,NO.241:124-126.
[19].苏丽,赵国良,张仁彦.基于改进小波阈值法的平移不变心电信号去噪.哈尔滨工程大学学报,2006,VOL.27(NO.6):839-843.
[20].吴少岩,许卓群.遗传算法中遗传算子的启发式构造策略.计算机学报,1998,VOL.21(NO.11):1003-1008.
[21].林宇,朴林华,张福学.全方位气体摆式倾角传感器的结构原理.电子元件与材料,2006,VOL.25(NO.5):19-22.
[22].郑德中,周颖慧,荆楠.基于GCV规则的小波图像去噪方法研究.仪器仪表学报,2006,VOL27(NO.6):2268-2270.
[23].胡昌华,李国华,刘涛,周志杰.基于MATLAB6.x的系统分析与设计—小波分析(第二版).西安市:西安电子科学大学出版社.2004.
[24].赵治栋,陈裕泉.广义小波收缩消噪阈值选择及应用研究.传感技术学报,2007,VOL.20(NO.3):601-605.
[25].赵婷婷,苑惠娟,邹纯宏.基于小波分析的阈值去噪改进算法.理论与方法,2007,VOL.26(NO.3):12-13.
[26].段玉倩,贺家李.遗传算法及其改进.电力系统及其自动化学报,1998,VOL.10(NO.1):39-51.
[27].陶杰,毕笃彦,苏涛,张海峰.基于平移不变和改进的广义交叉验证准则去噪研究.红外与激光工程,2005,VOL.34(NO.2):221-224.
[28].徐川育.提高变型标准遗传算法收敛速度的混合法及其推广.信息与控制,1997,VOL26(NO.4):266-271.
[29].徐洪泽,陈桂林,张福恩.遗传算法单双点交叉方法的比较研究.哈尔滨工业大学学报,1999,VOL.30(NO.2):63-71.
[30].盛骤,谢式千,潘承毅.概率论与数理统计(第二版).北京:高等教育出版社.2001.
[31].黄宜军,汪金友.信号去噪阈值参数选择的自适应退火遗传算法.测试技术学报,2006,VOL.20(NO.5):456-460.
[32].章珂,刘贵忠.交叉位置非等概率选取的遗传算法.信息与控制,1997,VOL.26(NO.1):53-60.
[33].雷英杰,张善文,李续武,周创明.MATLAB遗传算法工具箱及应用.西安:西安电子科技大学出版社.2005.
[34].Blickle T, Thiele L. A Comparison of Selection Schemes Used in the Genetic Algorithms. TIK-Report, Swiss Federal Institute of Technology(ETH), 1995.
[35].GP.Nason. Wavelet Shrinkage Using Cross-Validation. Journal of the Royal Statistical Society. Series B (Methodological). 1996,VOL58(NO.2):463-479.
[36].N. Weyrich and G T. Warhola. De-noising using wavelets and cross validation. Recent Developments in Approximation Theory, Wavelets and Applications. 1995,523-532.
[37].Norman Weyrich and Gregory T. Warhola. Wavelet Shrinkage and Generalized Cross Validation for Image Denoising. IEEE Transactions on Image Processing. 1998, VOL.7(NO.1):82-90.
[38].Srinivas M, Patnaik L.M. Adaptive Probabilities of Crossover and Mutations in GAs.IEEE Trans. on SMC, 1994,VOL24(NO.4);656-667.
[2].王大凯,彭进业小波分析及其在信号处理中的应用.北京:电子工业出版社.2006.
[3].王济,胡晓.MATLAB在振动信号处理中的应用.北京:中国水利水电出版社,知识产权出版社.2006.
[4].王小平,曹立明.遗传算法—理论、应用与软件实现.西安:西安交通大学出版社.2003.
[5].王广斌,何玉辉,黄良沛.排烟风机振动信号的小波阈值消噪及其折衷算法研究.有色设备,2006,NO.6:4-7.
[6].王裕鹏,赵龙庆,张华伟.基于小波改进阈值消噪的缸盖振动信号分析.柴油机,2007,VOL.29(NO.5):37-39.
[7].韦力强,谭阳红,贺迅宇,龙春阳.基于新阈值函数的小波阈去噪研究.计算技术与自动化,2006,VOL.25(NO.4):189-191.
[8].田小芳,陆起涌,熊超.基于加速度传感器的倾角仪设计.传感技术学报,2006,VOL.19(NO.2):361-363.
[9].冯轲.基于GCV规则的小波阈值去噪算法研究.科学之友,2007,10月刊:59-60.
[10].付炜,彭光剑.基于小波阈值去噪的改进方法.电子测量技术,2006,VOL.29(NO.6):46-48.
[11].阮沈勇,王永利,桑群芳.MATLAB程序设计.北京:电子工业出版社.2004.
[12].朴林华,张伟,张金铎,张福学.加速度对气体摆式倾角传感器影响机理的探讨.传感器技术,2004,VOL.23(NO.7):4-7.
[13].刘波,聂明新,向俊涛.基于小波分析的阈值语音信号去噪研究.现代计算机,2007,NO.257:20-22.
[14].汤爱芳,祝连庆,董明利.小波多分辨分析在超声回波信号去噪中的应用.计算机仿真,2006,VOL.23(NO.11):318-321
[15].李大卫,王梦光.一种改进的混合遗传算法.信息与控制,1997,VOL26(NO.6):449-454.
[16].邹鲲,袁俊泉,龚享铱.MATLAB6.x信号处理.北京:清华大学出版社.2002.
[17].陈进军,温志渝,文方,王武.液态电极电容式倾角传感器.仪器仪表学报,2004,VOL.25(NO.4):219-220.
[18].陈秋莲,王成栋.基于MATLAB遗传工具箱的优化计算实现.现代电子技 术,2006,NO.241:124-126.
[19].苏丽,赵国良,张仁彦.基于改进小波阈值法的平移不变心电信号去噪.哈尔滨工程大学学报,2006,VOL.27(NO.6):839-843.
[20].吴少岩,许卓群.遗传算法中遗传算子的启发式构造策略.计算机学报,1998,VOL.21(NO.11):1003-1008.
[21].林宇,朴林华,张福学.全方位气体摆式倾角传感器的结构原理.电子元件与材料,2006,VOL.25(NO.5):19-22.
[22].郑德中,周颖慧,荆楠.基于GCV规则的小波图像去噪方法研究.仪器仪表学报,2006,VOL27(NO.6):2268-2270.
[23].胡昌华,李国华,刘涛,周志杰.基于MATLAB6.x的系统分析与设计—小波分析(第二版).西安市:西安电子科学大学出版社.2004.
[24].赵治栋,陈裕泉.广义小波收缩消噪阈值选择及应用研究.传感技术学报,2007,VOL.20(NO.3):601-605.
[25].赵婷婷,苑惠娟,邹纯宏.基于小波分析的阈值去噪改进算法.理论与方法,2007,VOL.26(NO.3):12-13.
[26].段玉倩,贺家李.遗传算法及其改进.电力系统及其自动化学报,1998,VOL.10(NO.1):39-51.
[27].陶杰,毕笃彦,苏涛,张海峰.基于平移不变和改进的广义交叉验证准则去噪研究.红外与激光工程,2005,VOL.34(NO.2):221-224.
[28].徐川育.提高变型标准遗传算法收敛速度的混合法及其推广.信息与控制,1997,VOL26(NO.4):266-271.
[29].徐洪泽,陈桂林,张福恩.遗传算法单双点交叉方法的比较研究.哈尔滨工业大学学报,1999,VOL.30(NO.2):63-71.
[30].盛骤,谢式千,潘承毅.概率论与数理统计(第二版).北京:高等教育出版社.2001.
[31].黄宜军,汪金友.信号去噪阈值参数选择的自适应退火遗传算法.测试技术学报,2006,VOL.20(NO.5):456-460.
[32].章珂,刘贵忠.交叉位置非等概率选取的遗传算法.信息与控制,1997,VOL.26(NO.1):53-60.
[33].雷英杰,张善文,李续武,周创明.MATLAB遗传算法工具箱及应用.西安:西安电子科技大学出版社.2005.
[34].Blickle T, Thiele L. A Comparison of Selection Schemes Used in the Genetic Algorithms. TIK-Report, Swiss Federal Institute of Technology(ETH), 1995.
[35].GP.Nason. Wavelet Shrinkage Using Cross-Validation. Journal of the Royal Statistical Society. Series B (Methodological). 1996,VOL58(NO.2):463-479.
[36].N. Weyrich and G T. Warhola. De-noising using wavelets and cross validation. Recent Developments in Approximation Theory, Wavelets and Applications. 1995,523-532.
[37].Norman Weyrich and Gregory T. Warhola. Wavelet Shrinkage and Generalized Cross Validation for Image Denoising. IEEE Transactions on Image Processing. 1998, VOL.7(NO.1):82-90.
[38].Srinivas M, Patnaik L.M. Adaptive Probabilities of Crossover and Mutations in GAs.IEEE Trans. on SMC, 1994,VOL24(NO.4);656-667.