基于SVM算法的分布式光纤扰动传感系统快速检偏技术研究

Research on fast polarization detection technology of distributed optical fiber vibration sensing system using SVM algorithm

摘要:

针对基于双马赫-曾德尔干涉的分布式光纤扰动传 感系统对传感光信号的偏振状态需要进行实时监测以便及时进行偏振控制的问题,本文提出 一种基于支持向量机(SVM)的偏振状态快速判别算法。该方法对采集到的两路干涉信号进 行做差处理,得到原始数据。提取原始数据中的过零率与峰值的绝对和作为分类器的输入特 征向量,使用Verilog HDL语言编写支持向量机(SVM)分类识别算法,借助现场可编程门阵 列(FPGA)的硬件并行结构和流水线技术实现分类器权系数的快速迭代求解。通过提取未知 偏振状态信号的特征向量,并将其输入到训练好的支持向量机模型中可以实现高效率的偏振 状态识别。实验结果表明:本方法可以快速并准确的实现对系统偏振状态的识别判断,平均 识别率达到93.25%,分类模型训练时间在100 ms以内,平均识别响应时间在8ms以内。

Abstract:

Aiming at the problem that the distrib uted optical fiber vibration sensing system based on dual Mach-Zehnder interfer ometer needs to monitor the polarization state of the sensing optical signal in real time in order to carry out polarization control in time, an algorithm based on support vector machine (SVM) to quickly judge the polarization state has bee n proposed in this work.This method makes a difference between the two collected interference signals to obtain the original data.Extract the zero-crossing rat e and the absolute sum of peak value in the original data to construct the inpu t feature vector of the classifier,use Verilog HDL language to write SVM classif ication recognition algorithm,with the help of field-programmable gate array (F PGA) hardware parallel structure and Pipeline technology realize fast iterative solution of classifier weight coefficients.By extracting the feature vector of t he unknown polarization state signal and inputting it into the trained support v ector machine model,high-efficiency polarization state recognition can be achie ved.The experimental results show that this method can quickly and accurately ju dge the polarization state of the system.The average recognition rate of 93.25% is achieved,the training time of the classification model can be controlled less than 100ms,and the mean recognition response time is within 8ms.