基于CVMD和DBO-SVM的光纤周界安防信号识别方法

Fiber optic perimeter security signal recognition method based on CVMD and DBO-SVM

摘要:

为降低光纤周界安防信号中噪声对分类结果的影响，提升信号分类的准确率和运行效率，本文提出一种融合了相关变分模态分解(Correlation Variational Mode Decomposition，CVMD)、蜣螂算法(Dung Beetle Optimizer, DBO)和支持向量机(Support Vector Machine, SVM)的分类方法。利用CVMD去除原始信号中的噪声分量，并提取去噪后信号的能量、能量熵和峭度作为特征向量。采用DBO算法优化SVM，得到最佳惩罚因子和核函数参数，并构建DBO-SVM分类模型。搭建了基于相位敏感光时域反射(Φ-OTDR)技术的周界安防系统，采集了攀爬、敲击、踩踏和无入侵四类信号。实验结果表明，CVMD-DBO-SVM的分类准确率相比CVMD-PSO-SVM和CVMD-GA-SVM更高，达到了98.75%，同时运行时间更短，综合性能最优。

Abstract:

In order to reduce the influence of noise in the fiber optic perimeter security signal on the classification results and improve the accuracy and operating efficiency of signal classification, in this paper a classification method combine Correlation Variational Mode Decomposition (CVMD), Dung Beetle Optimizer (DBO), and Support Vector Machine (SVM) was proposed. CVMD was used to remove the noise component in the original signal. The energy, energy entropy and kurtosis of the denoised signal were extracted as feature vectors. The DBO algorithm was adopted to optimize the SVM to obtain the best penalty factor and kernel function parameters. The DBO-SVM classification model was constructed. A perimeter security system based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) technology was built to collect four types of signals: climbing, knocking, stepping and non-intrusion. The experimental results show that the classification accuracy of CVMD-DBO-SVM is higher than that of CVMD-PSO-SVM and CVMD-GA-SVM, reaching 98.75%. At the same time, the running time is shorter and the overall performance is the best.