提出一种基于过零率特征提取的多类别入侵事件识别方法,该方法对采集的入侵信号进行分段处理,并将每一段的过零率作为模式分类器的输入特征向量。使用Matlab编写支持向量机(SVM)分类识别算法,对大量入侵数据进行分类训练并保存模型参数,当外界有入侵时对新的未知事件进行特征向量提取并输入训练好的支持向量机模型中可以实现高效率高准确度模式识别。搭建了Michelson光纤周界安防系统,在户外围栏敷设2 km长的光缆进行实验验证。对剪切光缆、攀爬围栏、晃动围栏、敲击光缆和无入侵等5种不同的事件各取120组共600组实验。经实验验证,本方法可以快速并准确地识别这5种常见的事件信号。平均识别率达到97%,识别响应时间在0.1 s以内。

A recognition method for multiclass intrusion events based on zero-crossing rate feature extraction is proposed; in this approach, the intrusion signal is processed by segments, and the zero-crossing rate of each segment is used as the input feature vector for the pattern classifier. The support vector machine (SVM) classification and recognition algorithm is adopted to classify and train numerous intrusion data and save the model parameters. In unknown intrusion events, the feature vector is extracted and fed into the trained SVM model to realize high-efficiency and high-accuracy pattern recognition. A Michelson interferometer-based fiber perimeter security system is developed and a 2-km-long fiber optic cable is installed in the outdoor fence for experimental verification; 120 groups are used with a total of 600 experiments being performed under five different cases: shearing cable, climbing fence, swaying fence, tapping cable, and no intrusion. Experimental results confirm that the proposed method can quickly and accurately identify the tested types of common event signals; the average recognition rate reaches 97% and the response time is up to 0.1 s.