| 基于深度神经网络的光纤传感识别算法 |
| |
| 引用本文： | 李东亮,卢贝.基于深度神经网络的光纤传感识别算法[J].红外与激光工程,2022,51(9):20210971-1-20210971-6. |
| |
| 作者姓名： | 李东亮 卢贝 |
| |
| 作者单位： | 焦作大学 信息工程学院，河南 焦作 454000 |
| |
| 基金项目： | 河南省科技计划（202102310204） |
| |
| 摘 要： | 为解决光纤传感过程中不同类型事件信号混叠造成识别概率降低的问题，搭建了一种采用差分相关计算的双光纤传感结构，并在此基础上提出了基于深度神经网络的信号识别算法。首先利用双光纤回波信号计算相关系数，再通过不同事件类型信号特征设置阈值范围，从而通过相关计算与阈值滤波提高信噪比。设计了包含三个隐藏层的深度神经网络模型，以分离输入层与相关运算层的形式完成低频噪声抑制与信号混叠解调的目的。实验分别对三种常见入侵事件进行测试，并在此基础上分析了不同算法对组合事件的识别概率。结果显示三种事件的回波谱形具有显著特征。三种算法对单一触发事件的识别概率均在95%以上，该算法的识别均值为98.5%。当两个事件同时触发时，三种算法的平均识别概率分别为73.4%、 84.5%和96.4%。当三个事件同时触发时，三种算法的平均识别概率分别为65.2%、78.3%和93.5%。可见，该算法在光纤传感中信号存在干扰及混叠时具有更好的识别效果。
|
| 关 键 词： | 光纤传感 识别算法 深度神经网络 相关系数 |
| 收稿时间： | 2021-12-16 |
Optical fiber sensing recognition algorithm based on deep neural network |
| |
| Affiliation: | College of Information Engineering, Jiaozuo University, Jiaozuo 454000, China |
| |
| Abstract: | In order to solve the problem of reducing the recognition probability caused by the aliasing of different types of event signals in the optical fiber sensing process, a dual optical fiber sensing structure using differential correlation calculation is built. On this basis, a signal recognition algorithm based on deep neural network is proposed. First, the echo signal of the dual fiber is used to calculate the correlation coefficient. Then, the threshold range is set by signal characteristics of different event types, so as to improve the signal-to-noise ratio through correlation calculation and threshold filtering. A deep neural network model with three hidden layers is designed, and the purpose of low-frequency noise suppression and signal aliasing demodulation is accomplished by separating the input layer and the related operation layer. The experiments separately test three common intrusion events. The recognition probability of combined events by different algorithms is analyzed. The results show that the echo spectrum shape of the three events has significant characteristics. The recognition probability of the three algorithms is more than 95% for a single trigger event, and the average recognition value of this algorithm is 98.5%. When two events are triggered at the same time, the average recognition probabilities of the three algorithms are 73.4%, 84.5%, and 96.4%, respectively. When three events are triggered at the same time, the average recognition probabilities of the three algorithms are 65.2%, 78.3%, and 93.5%, respectively. It can be seen that this algorithm has a better recognition effect when there is interference and aliasing of signals in optical fiber sensing. |
| |
| Keywords: | |
|
| 点击此处可从《红外与激光工程》浏览原始摘要信息 |
|
点击此处可从《红外与激光工程》下载全文 |
|
