Li Kun, Zhen Yao, Li Peng, Hu Xinyue, Yang Lixia
School of Electronic Information Engineering, Anhui University, Hefei 230601, China.
8th Research Institute of China Electronics Technology Group Corporation, Hefei 230051, China.
Sensors (Basel). 2025 Mar 23;25(7):2016. doi: 10.3390/s25072016.
Accurately identifying optical fiber vibration signals is crucial for ensuring the proper operation of optical fiber perimeter security warning systems. To enhance the recognition accuracy of intrusion events detected by the distributed acoustic sensing system (DAS) based on phase-sensitive optical time-domain reflectometer (φ-OTDR) technology, we propose an identification method that combines empirical mode decomposition (EMD) with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. First, the EMD algorithm decomposes the collected original optical fiber vibration signal into several intrinsic mode functions (IMFs), and the correlation coefficient between each IMF and the original signal is calculated. The signal is then reconstructed by selecting effective IMF components based on a suitable threshold. This reconstructed signal serves as the input for the network. CNN is used to extract time-series features from the vibration signal and LSTM is employed to classify the reconstructed signal. Experimental results demonstrate that this method effectively identifies three different types of vibration signals collected from a real-world environment, achieving a recognition accuracy of 97.3% for intrusion signals. This method successfully addresses the challenge of φ-OTDR pattern recognition and provides valuable insights for the development of practical engineering products.
准确识别光纤振动信号对于确保光纤周界安全预警系统的正常运行至关重要。为提高基于相敏光时域反射仪（φ-OTDR）技术的分布式声学传感系统（DAS）检测到的入侵事件的识别准确率，我们提出一种将经验模态分解（EMD）与卷积神经网络（CNN）和长短期记忆（LSTM）网络相结合的识别方法。首先，EMD算法将采集到的原始光纤振动信号分解为若干固有模态函数（IMF），并计算每个IMF与原始信号之间的相关系数。然后基于合适的阈值选择有效的IMF分量对信号进行重构。这个重构后的信号作为网络的输入。CNN用于从振动信号中提取时间序列特征，LSTM用于对重构后的信号进行分类。实验结果表明，该方法能有效识别从实际环境中采集到的三种不同类型的振动信号，入侵信号的识别准确率达到97.3%。该方法成功解决了φ-OTDR模式识别的难题，为实际工程产品的开发提供了有价值的见解。
