COTDR信号去噪及曲线分析算法研究
|
摘要
随着光纤通信的快速发展,对网络的维护工作日益繁重。光时域反射仪OTDR可以通过分析OTDR回收的检测信号,有效反映出光纤中故障点的信息,确定出故障点的位置和类型,是光纤维护过程中的重要工具。由此发展出来的相干光时域反射仪COTDR,对光纤中返回的检测信号采用相干接收的方式,这样大大提升了检测信号的动态范围,使得COTDR可以检测长距离多跨段的光缆系统,比如海底光缆系统。但是,由于长距离光纤传输以及光缆系统中继EDFA的影响,使得回收的COTDR检测信号都带有很强的噪声,导致整个曲线无法有效反映光纤的状态信息。因此,关于这种多跨段COTDR检测信号的去噪和曲线分析就变得异常重要了。
本文首先对单跨段OTDR检测信号进行了介绍和仿真建模,在此基础上给出一种自适应阈值去噪处理算法,并作适当改进。该阈值去噪算法相较其它小波阈值去噪算法能够提升检测信号信噪比和曲线的动态范围；然后,对强噪声干扰下的多跨段COTDR检测信号进行分析,给出运用时域平均和上述改进小波阈值去噪相结合的方法,对实测数据进行去噪处理；最后根据COTDR检测信号的特殊性,本文进行了适当的曲线分析,包括检测曲线断点定位以及斜率分析。以上分析均通过仿真证明了其有效性。本文的主要工作包括：
(1)研究了OTDR的工作原理及其检测曲线的特点,并且仿真建模OTDR检测信号,运用一种改进的小波阈值去噪算法处理强噪声背景下的单跨段OTDR信号。
(2)研究多跨段实测COTDR检测信号数据,并运用时域平均和阈值去噪相结合的方法处理强噪声下的检测信号,有效对实测信号进行去噪处理。
(3)对去噪后的COTDR检测信号进行曲线分析,包括运用局部模极大值对多事件点的检测曲线进行断点定位以及运用最小二乘法对检测曲线进行斜率分析。
With the rapid development of Optical Fiber Communication, Network maintenance is essential. As an important tool of optical fiber maintenance, Optical Time Domain Reflectometer (OTDR) can analyze the detection signal, effectively reflect the breakpoint information, and determine the location and type of the breakpoint. On this basis, Coherent Optical Time Domain Reflectometer (COTDR) is developed, and it receive the returned signal by the way of coherent detection, which greatly enhance the dynamic range of the detection signal and make COTDR can detect long-distance and multi-span fiber links such as submarine cable system. However, as the impact of long-distance optical fiber transmission and EDFA repeater, the detection signals recovered by COTDR are with strong noise, and the detection curve can not effectively reflect the state of optical information. Therefore, denoising and curve analysis about COTDR detection signals become very important.
In this article, OTDR detection signals are introduced and modeled firstly. On this basis, an improved wavelet threshold denoising algorithm is proposed, which is better able to enhance snr of the signal and dynamic range of the curve than other denoising algorithms; Secondly, COTDR detection signals are analyzed under strong noise interference and the combination algorithm containing the time-domain average denoising and the above improved wavelet threshold denoising is proposed to deal with the real data; Finally, according to the particularity of COTDR detection signal, the article finish appropriate curve analysis, including breakpoint location and slope analysis. The above analysises are all proved through simulation. This major work includes:
(1) The working principle of OTDR and the detection curve characteristics are researched, two OTDR signals modeled, and an improved wavelet threshold denoising algorithm is proposed to deal with the signals under strong noise.
(2) Multi-span COTDR detection signals are researched, the time-domain average denoising and the improved wavelet threshold denoising are proposed to joint processing the real data under strong noise and the effect is good.
(3) Finish appropriate curve analysis after COTDR detection signals denoising, using wavelet modulus maxima to finish breakpoint location of the detection curve, and using the least square method to finish slope analysis of the curve.
本文首先对单跨段OTDR检测信号进行了介绍和仿真建模,在此基础上给出一种自适应阈值去噪处理算法,并作适当改进。该阈值去噪算法相较其它小波阈值去噪算法能够提升检测信号信噪比和曲线的动态范围；然后,对强噪声干扰下的多跨段COTDR检测信号进行分析,给出运用时域平均和上述改进小波阈值去噪相结合的方法,对实测数据进行去噪处理；最后根据COTDR检测信号的特殊性,本文进行了适当的曲线分析,包括检测曲线断点定位以及斜率分析。以上分析均通过仿真证明了其有效性。本文的主要工作包括：
(1)研究了OTDR的工作原理及其检测曲线的特点,并且仿真建模OTDR检测信号,运用一种改进的小波阈值去噪算法处理强噪声背景下的单跨段OTDR信号。
(2)研究多跨段实测COTDR检测信号数据,并运用时域平均和阈值去噪相结合的方法处理强噪声下的检测信号,有效对实测信号进行去噪处理。
(3)对去噪后的COTDR检测信号进行曲线分析,包括运用局部模极大值对多事件点的检测曲线进行断点定位以及运用最小二乘法对检测曲线进行斜率分析。
With the rapid development of Optical Fiber Communication, Network maintenance is essential. As an important tool of optical fiber maintenance, Optical Time Domain Reflectometer (OTDR) can analyze the detection signal, effectively reflect the breakpoint information, and determine the location and type of the breakpoint. On this basis, Coherent Optical Time Domain Reflectometer (COTDR) is developed, and it receive the returned signal by the way of coherent detection, which greatly enhance the dynamic range of the detection signal and make COTDR can detect long-distance and multi-span fiber links such as submarine cable system. However, as the impact of long-distance optical fiber transmission and EDFA repeater, the detection signals recovered by COTDR are with strong noise, and the detection curve can not effectively reflect the state of optical information. Therefore, denoising and curve analysis about COTDR detection signals become very important.
In this article, OTDR detection signals are introduced and modeled firstly. On this basis, an improved wavelet threshold denoising algorithm is proposed, which is better able to enhance snr of the signal and dynamic range of the curve than other denoising algorithms; Secondly, COTDR detection signals are analyzed under strong noise interference and the combination algorithm containing the time-domain average denoising and the above improved wavelet threshold denoising is proposed to deal with the real data; Finally, according to the particularity of COTDR detection signal, the article finish appropriate curve analysis, including breakpoint location and slope analysis. The above analysises are all proved through simulation. This major work includes:
(1) The working principle of OTDR and the detection curve characteristics are researched, two OTDR signals modeled, and an improved wavelet threshold denoising algorithm is proposed to deal with the signals under strong noise.
(2) Multi-span COTDR detection signals are researched, the time-domain average denoising and the improved wavelet threshold denoising are proposed to joint processing the real data under strong noise and the effect is good.
(3) Finish appropriate curve analysis after COTDR detection signals denoising, using wavelet modulus maxima to finish breakpoint location of the detection curve, and using the least square method to finish slope analysis of the curve.
引文
[1]褚波,丁铁骑.海底光缆在线检测与相干光时域反射仪技术.江苏通信技术,2001,17 (6):27-30.
[2]Blank L.C, Cox J.D. Optical Time Domain Reflectometry on Optical Amplifier Systems. Journal of Lightwave Technology,1989,7(10):1549-1555.
[3]程利军,王立军.基于光时域反射仪的光纤测试和分析.河北电力技术,2008,27(1)：42-43.
[4]邵群峰,漆晓琼,张晓萍.多跨距非线性光纤链路中的小波去噪.中国激光,2009,36(3)：592-596.
[5]仇胜美.海底光缆故障判定及测试方法.海洋工程,2005,23(3)：94-98.
[6]Kurashima.T, Tateda M, Shimizu K, Horiguchi T, Koyamada Y. A High Performance OTDR for Measuring Distributed Strain and Optical Loss, ECOC1996:215-218.
[7]Lun D P.K, Hsung T C. COTDR Performance Optimization for Amplified Transmission Systems. IEEE Photonics Technology Letters, July 1997, Vol.9, No.7: 1041-1043.
[8]李新华,梁浩,徐伟弘,张旭苹.常用分布式光纤传感器性能比较.光通信技术,2007,(5)：14-18.
[9]仇谷烽,高稚允.基于小波变换和最小二乘法的光纤衰减特性分析[J].光学技术,2000,26(5)：464-466.
[10]王韶波,李康,孔繁敏,吴涛.基于小波变换的光时域反射仪数据去噪分析.红外与激光工程,2003,32(4)：398-400.
[11]朱磊.一种基于小波变换的OTDR事件分析算法的研究[J].现代电子技术,2004,3(3)： 106-108.
[12]唐良瑞,祈兵,陈常洪,张根保.一种基于小波变换的光纤故障分析算法.中国电机工程学报,2006年,26(2)：101-105.
[13]王玉田,曹丽芳.小波分析在光时域反射计中的应用.传感器与微系统,2007,26(8)：114-120.
[14]Gu Xiaodong, Sablatash Mike. Estimation and Detection in OTDR Using Analyzing Wavelets. Proceedings of the IEEE-SP International Symposium on Time-Frequency And Time-Scale Analysis[J],1994:353-356.
[15]Zarowski C J. Connection Splice Fault Location in Fibre Optic Cables[C]. Canadian Conference on Electrical and Computer Engineering, May 1996, Vol.1:218-221.
[16]Fenglei Liu, Zarowski C J. Events in Fiber Optics Given Noisy OTDR-Data.I:GSR/MDL method[J]. IEEE Trans on Instrumentation and Measurement, 2001,50(1):47-58.
[17]Fenglei Liu, Zarowski C J. Detection and Location of Connection Splice Events in Fiber Optics Given Noisy OTDR Data-Part Ⅱ:RIMSDE Method[J]. IEEE Trans on Instrumentation and Measurement,2004,53(2):546-556.
[18]Yongmin Kim, Jinho Sung, Sung Ryul Hong, and Jaehong Park. Analyzing OTDR Measurement Dataa Using the Kalman Filter. IEEE Trans on Instrumentation and Measurement,2008,57(5):947-951.
[19]张新慧,彭丽芳,陈宗军,宫士营.利用重复采样技术提高OTDR中ADC量化精度.山东工程学院学报,2002,16(4)：34-37.
[20]胡清兰等.光时域计的特性参数与测量的几个问题.电力系统通信,1999,(1)：37-41.
[21]梁爽,王怀江OTDR事件分析和故障判断的研究和实现.光通信技术,2007,(1)：49-51.
[22]T. Kurashima, M. Tateda, T. Horiguchi, Y. Koyamada. Performance Improvement of a Combined OTDR for Distributed Strain and Loss Measurement by Randomizing the Reference Light Polarization State. IEEE Photonics Technology Letters, March 1997, Vol.9, NO.3:360-362.
[23]Nazarathy M, Newton S A, Giffard R P, et al. Real-time Long Range Complementary Correlation Optical Time Domain Reflectometer[J]. Journal of Lightwave Technology, 1989, Vol.1:24-37.
[24]王加强,岳新全,李勇.光纤通信工程[M].北京邮电大学出版社,2003：189-198.
[25]马正先,熊建文等OTDR在光纤测量中的应用.激光杂志,1999,20(2)：1-7.
[26]田国栋.基于OTDR技术的光纤测试方法探讨.现代电子技术,2009,(19)：99-101.
[27]郭建平OTDR的测试原理及注意事项.中国有线电视,2004,(15)：61-63.
[28]成晓林.利用OTDR进行精确的测量.四川通信技术,2001,31(1)：39-43.
[29][美]Michael J.Roberts著,胡剑凌译.信号与系统.机械工业出版社,2006：319-359.
[30]陈韶华,陈川,赵冬艳.包络谱提取的四种方法比较分析.水雷站与舰船防护,2008,16(4)：9-12.
[31]秦香敏,潘宏侠.小波变换在信号去噪中的应用.控制技术与测试,2007,(1)：46-48.
[32]Donoho D.L, Johnstone I. Ideal Spatial Adaptation By Wavelet Shrinkage. Biometrika, 1994.12,81(3):425-455.
[33]Xiaodong Gu, Ching Chu, Sablatash M. Optical Wavelet Domain Reflectometry. Time-Frequency and Time-Scale Analysis,1994:158-161.
[34]Yan-fang Sang, Dong Wang, Ji-chun Wu. An Improved Wavelet De-noising Method for Time Series Analysis. Sixth International Conference on Fuzzy Systems and Knowledge Discovery,2009, Vol.41:517-521.
[35]任伟建,康朝海,于镝,张正辉.小波变换在信号奇异性检测中的应用.自动化技术与应用,2005,24(1)：68-69.
[36]张维强,宋国乡.基于一种新的阈值函数的小波域信号去噪.西安电子科技大学学报,2004,2(2)：296-299.
[37]黄宜军,汪金友.小波分析在微弱信号测量中的应用研究.计量学报,2007,28(2)： 163-166.
[38]Donoho D.L. De-Noising by Soft-Thresholding. IEEE Transactions on Information Theory, May 1995, Vol.41, No.3:613-627.
[39]鲁亚光.自适应小波阈值去噪在光谱信号处理中的应用.光谱实验室,2004,21(3)：602-605.
[40]Donoho D.L, Johnstone I.M. Threshold Selection For Wavelet Shrinkage of Noisy Data. Engineering in Medicine and Biology Society,1994:24-25.
[41]Chang S.G, Bin Yu. Spatially Adaptive Wavelet Thresholding with Context Modeling for Image Denoising. IEEE Transactions on Image Processing, Sept.2000:1522-1531.
[42]朱艳芹,杨先麟.几种基于小波阈值去噪的改进方法.电子测试,2008,2(2)：18-21.
[43]张磊,潘泉,张洪才等.小波域滤波阈值参数c的选取.电子学报,2001,3(3)：400-402.
[44]Gu xiaohong, Cai jinhui. Study the Gibbs Phenomenon in the De-noiseing by Wavelet. Proceedings of the 7th World Congress on Intelligent Control and Automation, June 25-27,2008:7564-7566.
[45]Peng Jiang, Xiaohua Dai, Zhi Wang, Youxian Sun. the Study of Pulp Thickness Signal De-noise Method Based on Translation Invariance Wavelet Transform. Proceedings of the 5th World Congress on Intelligent Control and Automation, June 15-19,2004: 3822-3825.
[46]Furukawa S, Tanaka K, Koyamada Y, Sumida M. Enhanced Coherent OTDR for Long Span Optical Transmission Lines Containing Optical Fiber Amplifiers. IEEE Photonics Technology Letters,1995, Vol.7, No.5:540-542.
[47]李月,杨宝俊,谭力等.微弱周期脉冲信号的取样积分-混沌系统联合检测方法.电子与信息学报,2003,25(12)：1653-1657.
[48]谢苗苗,朱宗玖.基于时域积累平均和小波多分辨率分析方法的弱信号去噪.煤 矿机械,2009,30(8)：227-228.
[49]Lun D.P, Tai-chiu Hsung, Yuk-fan Ho. Wavelet Singularity Detection For Image Processing. Midwest Symposium on Circuits and Systems,2002, VOL.2:11-156-Ⅱ-159.
[50]XiaoliHao, Houjin Chen, Changli Wang. One Data Processing Method for Detecting Fibre Events[C]. International Conference on Signal Processing Proceedings, ICSP, Beijing, China,2004.
[51]S. Mallat, W L.Hwang. Singularity Detection and Processing with Wavelets. IEEE Transactions on Information Theory,1992,38(2):617-643.
[52]张翠芳.基于小波变换的模极大值降噪法的实现与改进.南京邮电大学学报,2009,29(1)：74-77.
[53]黄渤,代彩红,于家琳.光谱辐射照度标准灯的数据插值与曲线拟合标准研究.应用光学,2009,30(1)：44-49.
[2]Blank L.C, Cox J.D. Optical Time Domain Reflectometry on Optical Amplifier Systems. Journal of Lightwave Technology,1989,7(10):1549-1555.
[3]程利军,王立军.基于光时域反射仪的光纤测试和分析.河北电力技术,2008,27(1)：42-43.
[4]邵群峰,漆晓琼,张晓萍.多跨距非线性光纤链路中的小波去噪.中国激光,2009,36(3)：592-596.
[5]仇胜美.海底光缆故障判定及测试方法.海洋工程,2005,23(3)：94-98.
[6]Kurashima.T, Tateda M, Shimizu K, Horiguchi T, Koyamada Y. A High Performance OTDR for Measuring Distributed Strain and Optical Loss, ECOC1996:215-218.
[7]Lun D P.K, Hsung T C. COTDR Performance Optimization for Amplified Transmission Systems. IEEE Photonics Technology Letters, July 1997, Vol.9, No.7: 1041-1043.
[8]李新华,梁浩,徐伟弘,张旭苹.常用分布式光纤传感器性能比较.光通信技术,2007,(5)：14-18.
[9]仇谷烽,高稚允.基于小波变换和最小二乘法的光纤衰减特性分析[J].光学技术,2000,26(5)：464-466.
[10]王韶波,李康,孔繁敏,吴涛.基于小波变换的光时域反射仪数据去噪分析.红外与激光工程,2003,32(4)：398-400.
[11]朱磊.一种基于小波变换的OTDR事件分析算法的研究[J].现代电子技术,2004,3(3)： 106-108.
[12]唐良瑞,祈兵,陈常洪,张根保.一种基于小波变换的光纤故障分析算法.中国电机工程学报,2006年,26(2)：101-105.
[13]王玉田,曹丽芳.小波分析在光时域反射计中的应用.传感器与微系统,2007,26(8)：114-120.
[14]Gu Xiaodong, Sablatash Mike. Estimation and Detection in OTDR Using Analyzing Wavelets. Proceedings of the IEEE-SP International Symposium on Time-Frequency And Time-Scale Analysis[J],1994:353-356.
[15]Zarowski C J. Connection Splice Fault Location in Fibre Optic Cables[C]. Canadian Conference on Electrical and Computer Engineering, May 1996, Vol.1:218-221.
[16]Fenglei Liu, Zarowski C J. Events in Fiber Optics Given Noisy OTDR-Data.I:GSR/MDL method[J]. IEEE Trans on Instrumentation and Measurement, 2001,50(1):47-58.
[17]Fenglei Liu, Zarowski C J. Detection and Location of Connection Splice Events in Fiber Optics Given Noisy OTDR Data-Part Ⅱ:RIMSDE Method[J]. IEEE Trans on Instrumentation and Measurement,2004,53(2):546-556.
[18]Yongmin Kim, Jinho Sung, Sung Ryul Hong, and Jaehong Park. Analyzing OTDR Measurement Dataa Using the Kalman Filter. IEEE Trans on Instrumentation and Measurement,2008,57(5):947-951.
[19]张新慧,彭丽芳,陈宗军,宫士营.利用重复采样技术提高OTDR中ADC量化精度.山东工程学院学报,2002,16(4)：34-37.
[20]胡清兰等.光时域计的特性参数与测量的几个问题.电力系统通信,1999,(1)：37-41.
[21]梁爽,王怀江OTDR事件分析和故障判断的研究和实现.光通信技术,2007,(1)：49-51.
[22]T. Kurashima, M. Tateda, T. Horiguchi, Y. Koyamada. Performance Improvement of a Combined OTDR for Distributed Strain and Loss Measurement by Randomizing the Reference Light Polarization State. IEEE Photonics Technology Letters, March 1997, Vol.9, NO.3:360-362.
[23]Nazarathy M, Newton S A, Giffard R P, et al. Real-time Long Range Complementary Correlation Optical Time Domain Reflectometer[J]. Journal of Lightwave Technology, 1989, Vol.1:24-37.
[24]王加强,岳新全,李勇.光纤通信工程[M].北京邮电大学出版社,2003：189-198.
[25]马正先,熊建文等OTDR在光纤测量中的应用.激光杂志,1999,20(2)：1-7.
[26]田国栋.基于OTDR技术的光纤测试方法探讨.现代电子技术,2009,(19)：99-101.
[27]郭建平OTDR的测试原理及注意事项.中国有线电视,2004,(15)：61-63.
[28]成晓林.利用OTDR进行精确的测量.四川通信技术,2001,31(1)：39-43.
[29][美]Michael J.Roberts著,胡剑凌译.信号与系统.机械工业出版社,2006：319-359.
[30]陈韶华,陈川,赵冬艳.包络谱提取的四种方法比较分析.水雷站与舰船防护,2008,16(4)：9-12.
[31]秦香敏,潘宏侠.小波变换在信号去噪中的应用.控制技术与测试,2007,(1)：46-48.
[32]Donoho D.L, Johnstone I. Ideal Spatial Adaptation By Wavelet Shrinkage. Biometrika, 1994.12,81(3):425-455.
[33]Xiaodong Gu, Ching Chu, Sablatash M. Optical Wavelet Domain Reflectometry. Time-Frequency and Time-Scale Analysis,1994:158-161.
[34]Yan-fang Sang, Dong Wang, Ji-chun Wu. An Improved Wavelet De-noising Method for Time Series Analysis. Sixth International Conference on Fuzzy Systems and Knowledge Discovery,2009, Vol.41:517-521.
[35]任伟建,康朝海,于镝,张正辉.小波变换在信号奇异性检测中的应用.自动化技术与应用,2005,24(1)：68-69.
[36]张维强,宋国乡.基于一种新的阈值函数的小波域信号去噪.西安电子科技大学学报,2004,2(2)：296-299.
[37]黄宜军,汪金友.小波分析在微弱信号测量中的应用研究.计量学报,2007,28(2)： 163-166.
[38]Donoho D.L. De-Noising by Soft-Thresholding. IEEE Transactions on Information Theory, May 1995, Vol.41, No.3:613-627.
[39]鲁亚光.自适应小波阈值去噪在光谱信号处理中的应用.光谱实验室,2004,21(3)：602-605.
[40]Donoho D.L, Johnstone I.M. Threshold Selection For Wavelet Shrinkage of Noisy Data. Engineering in Medicine and Biology Society,1994:24-25.
[41]Chang S.G, Bin Yu. Spatially Adaptive Wavelet Thresholding with Context Modeling for Image Denoising. IEEE Transactions on Image Processing, Sept.2000:1522-1531.
[42]朱艳芹,杨先麟.几种基于小波阈值去噪的改进方法.电子测试,2008,2(2)：18-21.
[43]张磊,潘泉,张洪才等.小波域滤波阈值参数c的选取.电子学报,2001,3(3)：400-402.
[44]Gu xiaohong, Cai jinhui. Study the Gibbs Phenomenon in the De-noiseing by Wavelet. Proceedings of the 7th World Congress on Intelligent Control and Automation, June 25-27,2008:7564-7566.
[45]Peng Jiang, Xiaohua Dai, Zhi Wang, Youxian Sun. the Study of Pulp Thickness Signal De-noise Method Based on Translation Invariance Wavelet Transform. Proceedings of the 5th World Congress on Intelligent Control and Automation, June 15-19,2004: 3822-3825.
[46]Furukawa S, Tanaka K, Koyamada Y, Sumida M. Enhanced Coherent OTDR for Long Span Optical Transmission Lines Containing Optical Fiber Amplifiers. IEEE Photonics Technology Letters,1995, Vol.7, No.5:540-542.
[47]李月,杨宝俊,谭力等.微弱周期脉冲信号的取样积分-混沌系统联合检测方法.电子与信息学报,2003,25(12)：1653-1657.
[48]谢苗苗,朱宗玖.基于时域积累平均和小波多分辨率分析方法的弱信号去噪.煤 矿机械,2009,30(8)：227-228.
[49]Lun D.P, Tai-chiu Hsung, Yuk-fan Ho. Wavelet Singularity Detection For Image Processing. Midwest Symposium on Circuits and Systems,2002, VOL.2:11-156-Ⅱ-159.
[50]XiaoliHao, Houjin Chen, Changli Wang. One Data Processing Method for Detecting Fibre Events[C]. International Conference on Signal Processing Proceedings, ICSP, Beijing, China,2004.
[51]S. Mallat, W L.Hwang. Singularity Detection and Processing with Wavelets. IEEE Transactions on Information Theory,1992,38(2):617-643.
[52]张翠芳.基于小波变换的模极大值降噪法的实现与改进.南京邮电大学学报,2009,29(1)：74-77.
[53]黄渤,代彩红,于家琳.光谱辐射照度标准灯的数据插值与曲线拟合标准研究.应用光学,2009,30(1)：44-49.