A distributed optic fiber perimeter security system is proved to be an effective strategy for the security monitoring of some vital targets, such as power plants, power substations and telecommunication base stations. However, this method can hardly distinguish different categories of the intrusion behavior and is easily mis-triggered by different kinds of environmental interference. To distinguish different intrusion patterns and different interference events effectively, a vibration pattern recognition algorithm is proposed and demonstrated based on the merged Sagnac interferometer structure. The method consists of two parts: the pre-processing algorithm and the multi-layer perceptron neural networks (MLP-NNs). The pre-processing algorithm is applied to retrieve and extract the vibration signal from the captured source signal, and the MLP-NN is used to realize pattern recognition from each type of input. Typically, a high-dimensional vector group which contains hundreds of orders of vibration signal’s power frequency is obtained to cover as many signalized features as possible. Moreover, results of the experiment deployed on a 10 kilometer long perimeter fence in the transformer substation show that the proposed classification-based model achieves 97.6% classification accuracy in the test. Through multiple comparison tests, the proposed model gives a solid performance in the subsequent integrated evaluation to classify each intrusion pattern.

中文翻译：

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周边安全监控系统中的入侵行为分类方法

事实证明，分布式光纤外围安全系统是监视某些重要目标（如发电厂，变电站和电信基站）安全的有效策略。但是，这种方法几乎无法区分不同种类的入侵行为，并且很容易因不同类型的环境干扰而被误触发。为了有效地区分不同的入侵模式和不同的干扰事件，提出了一种基于合并的Sagnac干涉仪结构的振动模式识别算法并进行了演示。该方法包括两部分：预处理算法和多层感知器神经网络（MLP-NN）。预处理算法适用于从捕获的源信号中检索和提取振动信号，MLP-NN用于从每种类型的输入中实现模式识别。通常，将获得包含数百个振动信号功率频率的高维向量组，以覆盖尽可能多的信号化特征。此外，在变电站的10公里长的围墙上部署的实验结果表明，所提出的基于分类的模型在测试中达到了97.6％的分类精度。通过多次比较测试，提出的模型在随后的综合评估中对每个入侵模式进行分类时均表现出出色的性能。获得包含数百个振动信号功率频率的高维向量组，以覆盖尽可能多的信号化特征。此外，在变电站的10公里长的围墙上部署的实验结果表明，所提出的基于分类的模型在测试中达到了97.6％的分类精度。通过多次比较测试，提出的模型在随后的综合评估中对每个入侵模式进行分类时均具有良好的性能。获得包含数百个振动信号功率频率的高维向量组，以覆盖尽可能多的信号化特征。此外，在变电站的10公里长的围墙上部署的实验结果表明，所提出的基于分类的模型在测试中达到了97.6％的分类精度。通过多次比较测试，提出的模型在随后的综合评估中对每个入侵模式进行分类时均表现出出色的性能。