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.

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The phase-retrieval method with delayed calibration is widely used in fiber-optic sensing structures to extract the distributed vibration messages. Yet, the performance of this method is not satisfactory when sudden changes are introduced in sensing path, for the DC compensation is not a slow variable under this situation. To improve the adjustment of interferometric fiber-optic structure, a self-tracking phase retrieval method is proposed. In the novel algorithm, the static state is tracked by calibrating DC compensation value in each retrieval period. When tested in the real scene with a 20 km-long sensing fiber, the performance of the novel retrieval algorithm has been proved. Experimental results show that, compared with the conventional delayed calibration method, selftracking algorithm brings a better accuracy and uniformity performance in intrusion location test and shows a better adaptability to the environment.