为了解决周界入侵行为识别正确率低的问题,对经验模态分解算法进行改进,并将其用于光纤布拉格光栅周界入侵行为分类。该方法利用短时平均过零率从整体信号中提取入侵信号,采用两次极值波延拓抑制经验模态分解算法的端点效应,对入侵信号进行分解并提取有效分量的特征,引用支持向量机对入侵行为进行识别;在室外环境下分别对无入侵和攀爬、剪切、碰撞、触摸4种入侵行为进行分类与识别。结果表明,所提方法能有效识别不同的入侵行为,识别正确率大于96%。

To solve the problem of low recognition rate of perimeter intrusion behaviors, an improved empirical mode decomposition algorithm is used in the perimeter intrusion behaviors classification of fiber Bragg gratings. In this algorithm, the intrusion signal is extracted from the overall signal by using the short time average zero-crossing rate algorithm, and the double extreme wave prolongation is used to decompose the end effect of empirical mode decomposition algorithm. The improved algorithm is employed to decompose the intrusion signal and the characteristics of the effective components are extracted. Support vector machine is used to identify the intrusion behaviors. The nonintrusive behavior and four different invasion behaviors such as climbing, shearing, colliding, and touching are used to classify and recognize in outdoor environment. The results show that the proposed method can effectively identify different intrusion behaviors, and the recognition rate is greater than 96%.