A new detection and recognition method for optical fiber pre-warning system

Optical fiber pre-warning system (OFPS) is widely used in several fields. But there are massive vibration data which is varied in complicated surroundings of OFPS. This will be a challenge to locate and identify the vibrations accurately. A new detection and recognition method is established in this article. The method consists of two parts, detection and recognition. First, the presented detection method is based on the theory of constant false alarm rate (CFAR) and dilation and erosion (DE). The former can detect the harmful intrusion signals and the latter can eliminated some isolated interferences. Harmful intrusions can be located and the data quantity for further recognition is reduced by using the detection method. Second, a multi-feature recognition method is established in this article to determine the type of the intrusions. Typical signal features, such as average magnitude difference function (AMDF), pitch period (PP) and duty cycle (DC), are used to identify the vibrations generated by vehicles, machine and artificial intrusions. In order to check out the feasibility and validity of the proposed method, several tests were carried out in Rushan of Shan Dong, China. The results show that the proposed detection and recognition method can locate the harmful intrusions and identify the type of vibrations effectively.