小波变换理论在轨道信号检测中的应用与研究
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摘要
近年来,随着我国铁路事业的飞速发展,越来越多的电气化铁路出现在我国的轨道交通上,列车运行的速度也随之越来越高,这给广大人民的出行带来了极大的便利。为适应铁路快速发展的需求,对轨道信号检测的要求也就越来越高,这就需要研究一种更为准确、可靠和抗干扰性能好的检测方法来完成对铁路信号的实时分析与检测。
目前,我国电气化区段轨道电路有25 Hz交流计数电码轨道电路、75 Hz交流计数电码轨道电路以及移频轨道电路等多种制式共存。本文以电气化区段轨道电路中较为常用的25Hz交流计数信号和ZPW-2000A无绝缘移频轨道电路信号为例,对这两种信号进行分析研究。由于在现场环境下有各种干扰源的存在,本文首先对这两种信号进行时域和频域上的分析,并分析了干扰信号的噪声来源,然后分别利用小波分析方法对25 Hz交流计数信号和ZPW-2000A无绝缘移频轨道信号进行了降噪处理和检测。
对于25 Hz交流计数信号,本文首先采用了阈值去噪的方法对其进行降噪处理,其中分别采用了硬阈值、软阈值、半软阈值的去噪方法,并针对不同的方法给出了在Matlab环境下的仿真结果；同时,根据小波变换理论分别对25 Hz交流计数信号的理想信号和含噪信号分别进行了检测。
对于ZPW-2000A移频轨道信号,本文首先介绍了如何从区间实验室的模拟区间控制系统对ZPW-2000A无绝缘移频轨道信号进行数据的采集。在对移频轨道信号的降噪部分,本文分别使用了小波降噪和小波包降噪两种不同的方法,并给出仿真结果。仿真证明,小波及小波包对含噪移频轨道电路起到了良好的滤波作用,说明小波和小波包在对信号降噪方面具有优越性。对于移频轨道电路的检测部分,本文分别采取了比较常用的db5、db4、coif5小波对所采集的移频信号数据进行了五层分解分析,并进行了信号的判别。
通过仿真实验可以看出,利用小波分析对25 Hz交流计数信号进行检测时,它具有抗干扰能力强、误差小的特点,利用该方法能有效地克服传统依赖时域判别方法中存在的0,1阈值很难选取的缺点；而利用小波分析对ZPW-2000A无绝缘移频轨道信号的检测时,小波算法可以有效地提高频率检测精度和分辨率,这在实际工程运用中具有重要的现实意义与实用价值。小波变换检测方法可以作为当前检测方法的一种有效补充。
There are more and more electrified railways in rail transport systems with the rapid growth of China's railway industry, which has brought great convenience for people's traveling. And with the speed of train running higher and higher, new ways to detect orbital signal are required.In order to meet the needs of rapid development of the railway, it's necessary to study a reliable, accurate and a good anti-jamming detecting method to analyze railway signal in real-time.
At present, there are many formats of track circuit in the electrified section in China, such as 25 Hz AC counting signal,75 Hz AC counting signal, frequency-shift keying track circuits.As the track circuit of 25 Hz AC counting signal and ZPW-2000A non-insulated frequency-shift keying track circuit is used widely in electrified section, this paper take them as example.As there are various sources of interference in the field environment, this paper firstly analyze these two signals both in time domain and frequency domain, secondly use the theory of wavelets transformation theory to de-noise the noise-signal,finally analyze the signal of 25 Hz AC counting signal and frequency-shift keying track circuits.
For 25 Hz AC counting signal, this paper firstly deal with the noise using the threshold de-noising method, including the ways of a hard threshold value, soft threshold value and the half-soft threshold value method, and at last the Matlab simulation results were given. At the demodulation part, this paper use the method of wavelets transform theory to detect the ideal 25 Hz AC counting signal and noisy 25 Hz AC counting signal.
For ZPW-2000A non-insulated frequency-shift keying signal, this paper describes how to collect the data from ZPW-2000A non-insulated frequency-shift keying track circuit. At the part of de-noising for frequency-shift keying signal, this paper taking the methods of wavelets denoising and wavelet packet de-noising. From the simulation result, it get the conclusion that the noise signal was filtered out, which verified that the wavelets and wavelet packet's superiority in de-noising. For the part of demodulating and detecting the collected frequency-shift keying signal data, this paper using some regular kinds of wavelets algorithms to decompose the signal into five level, such as db4、db5、coif5 wavelets.
From the result of simulation experiments, it can see that using wavelets theory detecting 25 Hz AC counting signal has the characteristics of strong ability of resisting disturbing and small error. In addition, it can effectively overcomes the shortcoming of the traditional method, such as 0,1 threshold is hard to select; For ZPW-2000A non-insulated frequency-shift keying signal demodulation, wavelets algorithm can effectively improve the detecting accuracy and frequency resolution, which have an important practical significance and practical value. The way of using wavelets transformation theory detecting signal can be an effective complement to the current detecting methods.
目前,我国电气化区段轨道电路有25 Hz交流计数电码轨道电路、75 Hz交流计数电码轨道电路以及移频轨道电路等多种制式共存。本文以电气化区段轨道电路中较为常用的25Hz交流计数信号和ZPW-2000A无绝缘移频轨道电路信号为例,对这两种信号进行分析研究。由于在现场环境下有各种干扰源的存在,本文首先对这两种信号进行时域和频域上的分析,并分析了干扰信号的噪声来源,然后分别利用小波分析方法对25 Hz交流计数信号和ZPW-2000A无绝缘移频轨道信号进行了降噪处理和检测。
对于25 Hz交流计数信号,本文首先采用了阈值去噪的方法对其进行降噪处理,其中分别采用了硬阈值、软阈值、半软阈值的去噪方法,并针对不同的方法给出了在Matlab环境下的仿真结果；同时,根据小波变换理论分别对25 Hz交流计数信号的理想信号和含噪信号分别进行了检测。
对于ZPW-2000A移频轨道信号,本文首先介绍了如何从区间实验室的模拟区间控制系统对ZPW-2000A无绝缘移频轨道信号进行数据的采集。在对移频轨道信号的降噪部分,本文分别使用了小波降噪和小波包降噪两种不同的方法,并给出仿真结果。仿真证明,小波及小波包对含噪移频轨道电路起到了良好的滤波作用,说明小波和小波包在对信号降噪方面具有优越性。对于移频轨道电路的检测部分,本文分别采取了比较常用的db5、db4、coif5小波对所采集的移频信号数据进行了五层分解分析,并进行了信号的判别。
通过仿真实验可以看出,利用小波分析对25 Hz交流计数信号进行检测时,它具有抗干扰能力强、误差小的特点,利用该方法能有效地克服传统依赖时域判别方法中存在的0,1阈值很难选取的缺点；而利用小波分析对ZPW-2000A无绝缘移频轨道信号的检测时,小波算法可以有效地提高频率检测精度和分辨率,这在实际工程运用中具有重要的现实意义与实用价值。小波变换检测方法可以作为当前检测方法的一种有效补充。
There are more and more electrified railways in rail transport systems with the rapid growth of China's railway industry, which has brought great convenience for people's traveling. And with the speed of train running higher and higher, new ways to detect orbital signal are required.In order to meet the needs of rapid development of the railway, it's necessary to study a reliable, accurate and a good anti-jamming detecting method to analyze railway signal in real-time.
At present, there are many formats of track circuit in the electrified section in China, such as 25 Hz AC counting signal,75 Hz AC counting signal, frequency-shift keying track circuits.As the track circuit of 25 Hz AC counting signal and ZPW-2000A non-insulated frequency-shift keying track circuit is used widely in electrified section, this paper take them as example.As there are various sources of interference in the field environment, this paper firstly analyze these two signals both in time domain and frequency domain, secondly use the theory of wavelets transformation theory to de-noise the noise-signal,finally analyze the signal of 25 Hz AC counting signal and frequency-shift keying track circuits.
For 25 Hz AC counting signal, this paper firstly deal with the noise using the threshold de-noising method, including the ways of a hard threshold value, soft threshold value and the half-soft threshold value method, and at last the Matlab simulation results were given. At the demodulation part, this paper use the method of wavelets transform theory to detect the ideal 25 Hz AC counting signal and noisy 25 Hz AC counting signal.
For ZPW-2000A non-insulated frequency-shift keying signal, this paper describes how to collect the data from ZPW-2000A non-insulated frequency-shift keying track circuit. At the part of de-noising for frequency-shift keying signal, this paper taking the methods of wavelets denoising and wavelet packet de-noising. From the simulation result, it get the conclusion that the noise signal was filtered out, which verified that the wavelets and wavelet packet's superiority in de-noising. For the part of demodulating and detecting the collected frequency-shift keying signal data, this paper using some regular kinds of wavelets algorithms to decompose the signal into five level, such as db4、db5、coif5 wavelets.
From the result of simulation experiments, it can see that using wavelets theory detecting 25 Hz AC counting signal has the characteristics of strong ability of resisting disturbing and small error. In addition, it can effectively overcomes the shortcoming of the traditional method, such as 0,1 threshold is hard to select; For ZPW-2000A non-insulated frequency-shift keying signal demodulation, wavelets algorithm can effectively improve the detecting accuracy and frequency resolution, which have an important practical significance and practical value. The way of using wavelets transformation theory detecting signal can be an effective complement to the current detecting methods.
引文
[1]高继祥.铁路信号运营基础[M].北京：中国铁道出版社,2007.
[2]韩宝明,李学伟.高速铁路概论[M].北京：北京交通大学出版社,2008.
[3]李向国.高速铁路技术[M].北京：中国铁道出版社,2006.
[4]秦清根,朱爱红.铁路信号基础设备及原理[M].兰州：兰州交通大学,2009.
[5]广州铁路(集团)公司教育处,机务处.机务运用工班长[M].广州：广铁集团.2003.
[6]林瑜筠.电气化铁路信号设备[M].北京：中国铁道出版社,2006.
[7]林瑜筠.新型移频自动闭塞[M].北京：中国铁道出版,2003.
[8]董昱.区间信号与列车运行控制系统[M].北京：中国铁道出版社,2008.
[9]林瑜筠,张铁增.区间信号自动闭塞[M].北京：中国铁道出版社,2006.
[10]汪希时.微机控制通用式机车信号[M].北京：中国铁道出版社,1994.
[11]林瑜筠.机车信号车载系统和站内电码化[M].北京：中国铁道出版社,2008.
[12]邱宽民.JT1-CZ2000型机车信号车载系统[M].北京：中国铁道出版社,2007.
[13]李茁.机车信号自动识别与解调算法研究[D].哈尔滨：哈尔滨工程大学,2007.
[14]翟文涛.机车信号系统检测装置的设计与实现[D].哈尔滨：哈尔滨工业大学,2007.
[15]樊昌信,张甫翎,徐炳吉.通信原理[M].北京：国防工业出版社,2006.
[16]客运专线铁路信号产品暂行技术条件-ZPW-2000A无绝缘轨道电路设备[S].2009.
[17]张辉,曹丽娜.通信原理[M].北京：科学出版社,2007.
[18]于珊珊.基于DSP的移频轨道信号参数检测方法的研究与实现[D].哈尔滨：哈尔滨工业大学,2006.
[19]乔智峰.机车轨道信号采集与处理技术研究[D].哈尔滨：哈尔滨工程大学,2007.
[20]赵军民.基于DSP高精度移频信号检测技术的研究与实现[D].成都：电子科技大学,2007.
[21]黄济荣,孙流芳.电力牵引交流传动与控制[M].北京：机械工业出版社,1998.
[22]Richard G.Lyonsi(美).数字信号处理[M].北京：机械工业出版社,2006.
[23]程佩青.数字信号处理教程[M].北京：清华大学出版社,2007.
[24]Mark A.Pinsky(美).傅里叶分析与小波分析导论[M].北京：机械工业出版社,2003.
[25]刘涛,曾祥利,曾军.实用小波分析入门[M].北京：国防工业出版社,2006.
[26]Dwight Mix Kraig J.Olejniczak. Elements of Wavelets for Engineers and Scientists[M].北京：机械工业出版社,2006.
[27]张德丰.MATLAB小波分析[M].北京：机械工业出版社,2009.
[28]陈仲英,巫斌.小波分析[M].北京：科学出版社,2007.
[29]唐向宏,李齐良.时频分析与小波变换[M].北京：科学出版社,2008.
[30]Vetterli M. Multi-dimensional Subband Coding:Some Theory and Algorithms[J].Signal Processing, 1984,(6):97-122.
[31]Daubechies Ⅰ, Lagarias J C. Two-scale difference equations Ⅰ:existence and global regularity of Solution. SIAM J. Math [J]. Anal,1991,22(5):1383-1400.
[32]Daubechies Ⅰ. Orthonormal Basees of Compactly Supported Wavelets Ⅱ Variations on a Theme.SIAM J.Math [J].Anal,1993,24(2):499-519.
[33]关履泰.小波方法与应用[M].北京：高等教育出版社,2007.
[34]Jaideva C Goswami, Andrew K Chan(美).小波分析理论、算法及其应用[M].北京：国防工业出版社,2007.
[35]Mallat S. Multifrequency Channel Decomposition of Images and Wavelet Models[J]. IEEE Trans PAMI,2003,14(7):710-732.
[36]Soman A K,Vailyanathan P P. Paraunitary Filter Banks and Wavelet packets[J].ICASSP,1992,(4):387-400.
[37]Strichartz R S.How to Make Wavelet.Amer.Math[J].Anal,1993,(100):539-556.
[38]Chi C K, Li C.Nonorthogonal Wavelet Packets[J]. SIAM J.Math Anal,1993,(24):712.
[39]蔡浩江.小波理论在图像扩散中的应用[D].西安：西安电子科技大学,2009.
[40]潘泉,张磊,孟晋丽.小波滤波方法及应用[M].北京：清华大学出版社,2005.
[41]段红,魏俊民.现代测试信号处理理论与实践tM].北京：中国纺织出版社,2005.
[42]Donoho David L. Denoising by Soft-thresholding[J].IEEE Transactions on Information Theory, 1999,Voll(2):115-122.
[43]闫健.利用时频分析的轨道FSK参数检测技术的研究[D].哈尔滨：哈尔滨理工大学,2007.
[44]周伟,桂林,周林等.Matlab小波分析高级技术[M].西安：西安电子科技大学出版社,2006.
[45]葛哲学,沙威.小波分析理论与MATLAB R2007实现[M].北京：电子工业出版社,2007.
[46]曹弋.MATLAB教程及实训[M].北京：机械工业出版社,2008.
[47]Diao Wen Jing. A New Kind of Arithmetic for Baud-Rate Estimation with Combination of Wavelet Transform and FFT[J].Journal of Detection&Control,2006,(2):30-34.
[48]张小蓟.快速小波算法在机车信号检测中的应用[J].计算机工程与应用,2008,44(24)：222-227.
[49]汤长春.轨道移频信号特征频率提取[D].武汉：武汉科技大学,2007.
[2]韩宝明,李学伟.高速铁路概论[M].北京：北京交通大学出版社,2008.
[3]李向国.高速铁路技术[M].北京：中国铁道出版社,2006.
[4]秦清根,朱爱红.铁路信号基础设备及原理[M].兰州：兰州交通大学,2009.
[5]广州铁路(集团)公司教育处,机务处.机务运用工班长[M].广州：广铁集团.2003.
[6]林瑜筠.电气化铁路信号设备[M].北京：中国铁道出版社,2006.
[7]林瑜筠.新型移频自动闭塞[M].北京：中国铁道出版,2003.
[8]董昱.区间信号与列车运行控制系统[M].北京：中国铁道出版社,2008.
[9]林瑜筠,张铁增.区间信号自动闭塞[M].北京：中国铁道出版社,2006.
[10]汪希时.微机控制通用式机车信号[M].北京：中国铁道出版社,1994.
[11]林瑜筠.机车信号车载系统和站内电码化[M].北京：中国铁道出版社,2008.
[12]邱宽民.JT1-CZ2000型机车信号车载系统[M].北京：中国铁道出版社,2007.
[13]李茁.机车信号自动识别与解调算法研究[D].哈尔滨：哈尔滨工程大学,2007.
[14]翟文涛.机车信号系统检测装置的设计与实现[D].哈尔滨：哈尔滨工业大学,2007.
[15]樊昌信,张甫翎,徐炳吉.通信原理[M].北京：国防工业出版社,2006.
[16]客运专线铁路信号产品暂行技术条件-ZPW-2000A无绝缘轨道电路设备[S].2009.
[17]张辉,曹丽娜.通信原理[M].北京：科学出版社,2007.
[18]于珊珊.基于DSP的移频轨道信号参数检测方法的研究与实现[D].哈尔滨：哈尔滨工业大学,2006.
[19]乔智峰.机车轨道信号采集与处理技术研究[D].哈尔滨：哈尔滨工程大学,2007.
[20]赵军民.基于DSP高精度移频信号检测技术的研究与实现[D].成都：电子科技大学,2007.
[21]黄济荣,孙流芳.电力牵引交流传动与控制[M].北京：机械工业出版社,1998.
[22]Richard G.Lyonsi(美).数字信号处理[M].北京：机械工业出版社,2006.
[23]程佩青.数字信号处理教程[M].北京：清华大学出版社,2007.
[24]Mark A.Pinsky(美).傅里叶分析与小波分析导论[M].北京：机械工业出版社,2003.
[25]刘涛,曾祥利,曾军.实用小波分析入门[M].北京：国防工业出版社,2006.
[26]Dwight Mix Kraig J.Olejniczak. Elements of Wavelets for Engineers and Scientists[M].北京：机械工业出版社,2006.
[27]张德丰.MATLAB小波分析[M].北京：机械工业出版社,2009.
[28]陈仲英,巫斌.小波分析[M].北京：科学出版社,2007.
[29]唐向宏,李齐良.时频分析与小波变换[M].北京：科学出版社,2008.
[30]Vetterli M. Multi-dimensional Subband Coding:Some Theory and Algorithms[J].Signal Processing, 1984,(6):97-122.
[31]Daubechies Ⅰ, Lagarias J C. Two-scale difference equations Ⅰ:existence and global regularity of Solution. SIAM J. Math [J]. Anal,1991,22(5):1383-1400.
[32]Daubechies Ⅰ. Orthonormal Basees of Compactly Supported Wavelets Ⅱ Variations on a Theme.SIAM J.Math [J].Anal,1993,24(2):499-519.
[33]关履泰.小波方法与应用[M].北京：高等教育出版社,2007.
[34]Jaideva C Goswami, Andrew K Chan(美).小波分析理论、算法及其应用[M].北京：国防工业出版社,2007.
[35]Mallat S. Multifrequency Channel Decomposition of Images and Wavelet Models[J]. IEEE Trans PAMI,2003,14(7):710-732.
[36]Soman A K,Vailyanathan P P. Paraunitary Filter Banks and Wavelet packets[J].ICASSP,1992,(4):387-400.
[37]Strichartz R S.How to Make Wavelet.Amer.Math[J].Anal,1993,(100):539-556.
[38]Chi C K, Li C.Nonorthogonal Wavelet Packets[J]. SIAM J.Math Anal,1993,(24):712.
[39]蔡浩江.小波理论在图像扩散中的应用[D].西安：西安电子科技大学,2009.
[40]潘泉,张磊,孟晋丽.小波滤波方法及应用[M].北京：清华大学出版社,2005.
[41]段红,魏俊民.现代测试信号处理理论与实践tM].北京：中国纺织出版社,2005.
[42]Donoho David L. Denoising by Soft-thresholding[J].IEEE Transactions on Information Theory, 1999,Voll(2):115-122.
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