In this paper, we propose an improved feature extraction based multiple events recognition scheme for fiber optic perimeter security system. In the scheme, four common types of security sensing events, namely, background noises, waggling the fence, cutting the fence and climbing the fence are collected based on a dual Mach-Zehnder interferometry vibration sensor. Variational mode decomposition in frequency domain, sample entropy in irregularity and zero crossing rate in time domain are considered as the feature description of the given security sensing events. A series of experiments have been implemented by a radial basis foundation neural network, which shows that the proposed recognition scheme can accurately discriminate the three kinds of man-made intrusions from the background noises. The average identification rates of 98.42% and 100% are achieved for the three types of intrusions and background noises, respectively, which can fully satisfy the field application requirements, the recognition response time is also good of real time performance, which can be controlled less than 1.6 s. Therefore, the proposed events recognition scheme can provide a quite promising field application prospect in the fiber optic perimeter security system.
We propose a variational mode decomposition (VMD)-based endpoint detection method for distributed fiber interferometric vibration sensing systems. First, the interference signal is decomposed into two number of modes (intrinsic mode functions (IMF1 and IMF2)). Then, the time moment corresponding to the disturbance starting point can be obtained using threshold judgment to IMF2. Finally, an experiment using a dual-laser source Mach-Zehnder interferometers (DSMZI)-based system is performed. Experimental results demonstrate that the error of the proposed scheme is 2 orders of magnitude lower than the conventional zero-crossing ratio (ZCR)-based method at the sensing length of 85 km. The mean processing time is 0.167 s, which is less than the sampling time of 0.3 s. Therefore, this high- efficiency endpoint detection method has potential practical applications in distributed fiber interferometric vibration sensing systems.
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