基于马尔可夫转移场的Φ-OTDR信号模式识别  

Pattern Recognition of Φ⁃OTDR Signals Based on Markov Transition Field

作　　者：梅琅 郭灿 梁磊 Mei Lang;Guo Can;Liang Lei(National Engineering Research Center for Optical Fiber Sensing Technology and Network,Wuhan 430070,Hubei,China;Department of Physics,School of Science,Wuhan University of Technology,Wuhan 430070,Hubei,China;School of Computer Science and Artificial Intelligence,Wuhan University of Technology,Wuhan 430070,Hubei,China;School of Mechanical and Electrical Engineering,Wuhan University of Technology,Wuhan 430070,Hubei,China)

机构地区：[1]光纤传感技术与网络国家工程研究中心,湖北武汉430070 [2]武汉理工大学理学院物理系,湖北武汉430070 [3]武汉理工大学计算机与人工智能学院,湖北武汉430070 [4]武汉理工大学机电工程学院,湖北武汉430070

出　　处：《中国激光》2025年第1期185-195,共11页Chinese Journal of Lasers

摘　　要：相位敏感光时域反射计(Φ-OTDR)系统应用场景广泛,感知的信号类型复杂多变,因此对于Φ-OTDR信号识别方法的研究至关重要。为提高识别准确率和实时性,提出了一种基于马尔可夫转移场和MobileNetV2的Φ-OTDR信号模式识别方法。该方法基于马尔可夫转移场原理将一维序列信号编码成二维图像,能更好地挖掘出信号的动态转换特征,并且省去了传统模式识别任务中复杂的特征提取步骤。马尔可夫转移场图像特征密集,细节复杂,MobileNetV2网络对这种特殊的编码图像表现出了较好的识别性能。该方法对6种Φ-OTDR信号识别的平均准确率为96.0%,对单个Φ-OTDR信号识别时间为0.0340s。Objective Phase sensitive optical time-domain reflectometer(Φ-OTDR)system has a wide range of application scenarios,and the types of perceived signals are complex and varied.Therefore,research on the recognition method ofΦ-OTDR signals is crucial.To improve recognition accuracy and achieve shorter recognition time,a pattern recognition method based on Markov transition field(MTF)and MobileNetV2 for theΦ-OTDR signal is proposed.Methods We first decompose the two-dimensionalΦ-OTDR spatiotemporal signal into a set of one-dimensional signals,and use downsampling to shorten the length of the original signal and reduce the amount of data.Next,based on the MTF principle,the preprocessed one-dimensional signal is encoded into a two-dimensional image.This image encoding method has good noise resistance characteristics and can amplify and capture the time-domain features of one-dimensional signals.The encoded image is input into four lightweight neural network models for signal pattern recognition.The experimental results indicate that MobileNetV2 has the best recognition performance for encoded images.Finally,transfer learning methods are used to train the network model,effectively accelerating the convergence of the model and improving the recognition accuracy.Results and Discussions This method achieves high recognition accuracy and fast recognition speed,with an average recognition accuracy of 96.0%for six signals and recognition time of 0.2047 s for a single signal.Among the six signal modes,the method proposed in this paper has high recognition accuracy for the four signal modes of digging,knocking,watering,and walking.Comparing the proposed method with the latest research on recognition,as shown in Table 3,it demonstrates advantages in average recognition accuracy compared to traditional convolutional neural network(CNN)methods and particle swarm optimizationsupport vector machine(PSO-SVM)based methods.The method presented in this paper demonstrates better classification performance for the four types ofΦ-OTDR

关 键 词：光纤光学 光纤传感 相位敏感光时域反射计 马尔可夫转移场 模式识别 

分 类 号：TN247[电子电信—物理电子学]