针对相位敏感光时域反射计(φ-OTDR)分布式光纤扰动传感系统对扰动事件进行有效判别和识别的问题, 提出一种基于支持向量机(SVM)的扰动判别和扰动模式识别的方法。通过提取信号时域和频域的平均值、方差、均方差以及信号功率特征, 利用二叉树结构建立基于SVM算法的分类器, 对扰动进行判别并对扰动模式进行识别。根据传感信号的特征, 通过分类器I在对有无扰动信号进行判别的基础上, 进一步对有扰动信号利用分类器对扰动事件的模式进行识别。通过实验对所提出的方法进行验证, 对600组实验数据进行扰动判别和模式识别, 正确的扰动判别率在96%以上, 漏报率和误报率在4%以下; 正确的模式识别率均在94%以上。

Currently, phase sensitive optical time-domain reflectometer (φ-OTDR) distributed optical fiber sensing system is difficult to accurately determine current position of disturbance and distinguish the model of disturbance effectively. A method was proposed based on support vector machine (SVM) which can accurately distinguish disturbance and the model of disturbance. With the technique of the binary tree, a categorizer based on SVM was set up by extracting the various signal characteristics of the mean, the variance, the mean square deviation and energy of the time- domain and frequency-domain. Thus the disturbance and disturbance mode can be distinguished. In terms of the sensing signal feature, the categorizer I was determined if the sensing signals was disturbance signals or not firstly. Then, mode of disturbance can be recognized by the following categorizers. Experiments were carried out to validate the proposed method by 600 groups of data. The correct discrimination rate is better than 96%. The rate of missing report and the rate of false positives is less than 4%. The rate of correct pattern recognition is greater than 94%.