基于反相双峰指数模型的微弱瞬态极低频信号的估计与检测
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摘要
在铁磁管道环境下,同频窄带噪声中的微弱瞬态极低频信号的估计与检测问题是管道机器人跟踪定位中最为重要的科学问题之一。为实现窄带噪声中的微弱瞬态信号的有效检测,分析了接收线圈在不同空间姿态下的信号特点,针对信号与窄带噪声相位上的区别以及信号包络的形态,建立了相位反相的双峰指数函数数学模型;应用非线性最小二乘估计,实现了该数学模型和真实信号之间的拟合;通过仿真和实验验证了该数学模型与真实信号的高度匹配性,并应用蒙特卡洛仿真分析了该模型参数估计的性能;使用所建立的数学模型,构建了平均功率检测器和瞬时最大功率检测器,通过分析比较极低频发射机在不同移动速度和接收信噪比条件下的检测性能,得出了两种检测器的特点和适用范围,并指出在现有工程背景下平均功率检测器的优势。实验证明,相位反相信息对同频窄带噪声中的信号检测非常重要,平均功率检测器在低信噪比条件下的检测性能良好。在信噪比为0.05 d B、虚警概率设为1%时,该检测器的检测概率达98.8%。
It is an important problem in the localization system of pipeline robot that the estimation and detection of weak transient extremely low frequency( ELF) signal with the same frequency narrowband noise. In order to efficiently detect the signal,the characteristic of the received signal is analyzed when the coil transducer is in different space posture,and mathematic model is built with a doublepeak exponential function with phase jump by distinguishing the phase's characteristic between the signal and the noise in same frequency narrowband,as well as the envelope of the signal. The good similarity between the real signal and the mathematic model is proved in the simulation and the experiment. Then,according to nonlinear least square estimation,the matching between the real signal and the mathematic model is realized,and the performance of estimation is analyzed by Monte-Carlo simulation. Based on the estimated mathematic model,a mean power detector and a maximum power detector are built respectively. Their feature and circumstances of application are given by analyzing their performance at different speed of the ELF transmitter and background noise. It is proved that the mean power detector is much better than the maximum power detector under the current engineering conditions. The experimental results show that the phase jump is important to distinguish the signal and the noise as well as to guarantee the good performance at low Signal to Noise Ratio( SNR) for the mean power detector. The detection probability of this detector could be as high as 98. 8% under the conditions in which the SNR is 0. 05 d B and false alarm probability is 1 %.
It is an important problem in the localization system of pipeline robot that the estimation and detection of weak transient extremely low frequency( ELF) signal with the same frequency narrowband noise. In order to efficiently detect the signal,the characteristic of the received signal is analyzed when the coil transducer is in different space posture,and mathematic model is built with a doublepeak exponential function with phase jump by distinguishing the phase's characteristic between the signal and the noise in same frequency narrowband,as well as the envelope of the signal. The good similarity between the real signal and the mathematic model is proved in the simulation and the experiment. Then,according to nonlinear least square estimation,the matching between the real signal and the mathematic model is realized,and the performance of estimation is analyzed by Monte-Carlo simulation. Based on the estimated mathematic model,a mean power detector and a maximum power detector are built respectively. Their feature and circumstances of application are given by analyzing their performance at different speed of the ELF transmitter and background noise. It is proved that the mean power detector is much better than the maximum power detector under the current engineering conditions. The experimental results show that the phase jump is important to distinguish the signal and the noise as well as to guarantee the good performance at low Signal to Noise Ratio( SNR) for the mean power detector. The detection probability of this detector could be as high as 98. 8% under the conditions in which the SNR is 0. 05 d B and false alarm probability is 1 %.
引文
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[2]黄杏,张奇贤.浅海中电偶极子极低频磁场衰减特性研究[J].国外电子测量技术,2013,32(7):31-33.HUANG X,ZHANG Q X.Attenuation properties of ELF magnetic field in the shallow sea water[J].Foreign Electronic Measurement Technology,2013,32(7):31-33.
[3]QI H,YE J,ZHANG X,et al.Wireless tracking and locating system for in-pipe robot[J].Sensors and Actuators A:Physical,2010,159(1):117-125.
[4]QI H,ZHANG X,CHEN H,et al.Tracing and localization system for pipeline robot[J].Mechatronics,2009,19(1):76-84.
[5]王淳,郭静波,刘红旗,等.基于最小二乘的极低频微弱信号实时检测方法[J].仪器仪表学报,2009,30(12):2468-2473.WANG CH,GUO J B,LIU H Q,et al.Novel approach of real-time ELF weak signal detection based on least square algorithm[J].Chinese Journal of Scientific Instrument,2009,30(12):2468-2473.
[6]陈水平,郭静波,胡铁华.铁磁管道环境下极低频微弱磁场的分布及检测[J].仪器仪表学报,2011,32(10):2348-2356.CHEN SH P,GUO J B,HU T H.Research on extremely low frequency transmitting and receiving system in pipeline environment[J].Chinese Journal of Scientific Instrument,2011,32(10):2348-2356.
[7]魏明生,童敏明,訾斌,等.基于粒子群-拟牛顿混合算法的管道机器人定位[J].仪器仪表学报,2013,33(11):2594-2600.WEI M SH,TONG M M,ZI B,et al.Pipeline robot localization system based on PSO-BFGS hybrid algorithm[J].Chinese Journal of Scientific Instrument,2013,33(11):2594-2600.
[8]杜云朋,王建斌,靳小强.超声导波管道检测的小波模极大值去噪法[J].电子测量与仪器学报,2013,27(7):683-687.DU Y P,WANG J B,JIN X Q.Defect detection of ultrasonic guided wave pipeline using de-noising method basedon wavelet modulus maximum[J].Journal of Electronic Measurement and Instrument,2013,27(7):683-687.
[9]耿欣,曲兴华,江炜,等.大型管道快速测量与建模技术研究及应用[J].仪器仪表学报,2013,34(2):338-343.GENG X,QU X H,JIANG W,et al.Rapid measurement and modeling technologies of large pipes and their application[J].Chinese Journal of Scientific Instrument,2013,34(2):338-343.
[10]KAY S M.Fundamentals of Statistical signal processing,Volume 2:Detection theory[M].Prentice Hall PTR,1998.
[11]NUTTALL A H.Detection performance of power-law processors for random signals of unknown location,structure,extent,and strength[R].Naval Undersea Warfare Center Newport DIV RI,1994.
[12]NUTTALL A H.Near-optimum detection performance of power-law processors for random signals of unknown locations,structure,extent,and arbitrary strengths[R].Naval Undersea Warfare Center Newport Div New London Ct New London Detachment,1996.
[13]PAGE E S.Continuous inspection schemes[J].Biometrika,1954:100-115.
[14]PAGE E S.A test for a change in a parameter occurring at an unknown point[J].Biometrika,1955,42(3-4):523-527.
[15]WANG ZH,WILLETT P K.All-purpose and plug-in power-law detectors for transient signals[J].IEEE Transactions on Signal Processing,2001,49(11):2454-2466.
[16]AGILI S,BJORNBERG D B,MORALES A.Optimized search over the Gabor dictionary for note decomposition and recognition[J].Journal of the Franklin Institute,2007,344(7):969-990.
[17]KONG A W K.An analysis of Gabor detection[M].Image Analysis and Recognition,2009:64-72.
[18]张刚,胡韬,王颖.基于Melnikov函数Duffing系统微弱信号检测[J].电子测量技术,2015,38(1):109-112.ZHANG G,HU T,WANG Y.The weak signal detection based on doffing system and melnikov function[J].Electronic Measurement Technology,2015,38(1):109-112.
[19]吴冬梅.基于达芬振子的微弱信号检测方法研究[D].哈尔滨:哈尔滨工程大学,2010.WU D M.Research on methodology of weak signal detection based on Duffing oscillator[D].Harbin:Harbin Engineering University,2010.
[20]刘海波,吴德伟,金伟,等.Duffing振子微弱信号检测方法研究[J].物理学报,2013,62(5):42-47.LIU H B,WU D W,JIN W,et al.Study on weak signal detection method with Duffing oscillators[J].Acta Physicaa Sinica,2013,62(5):42-47.