Events detection and recognition by the fiber vibration system based on power spectrum estimation

Abstract

One of the important successes of optical fiber sensor established for the security system is the detection and the recognition of any type of events. The performance parameters (event recognition, event detection position, and time of detection) are unavoidable and describe the validity of any perimeter detection system. An event recognition is any signal detected within the protected area, and it is related to a non-intrusion event and an intrusion event. To achieve the detection and the recognition events at the real time, an effective two-level vibration recognition method and a technique are proposed and presented in this article. The signal characteristics (short-term energy and short-time overthreshold) have been used and compared to the dynamic threshold to judge the type of event. Then the extraction of the power distribution features on the frequency domain through power spectral estimation on the suspected intrusion signal samples is carried out and finally combined with the time-domain characteristics as feature vector through Support Vector Machine to determine the efficiency and effectiveness of the proposed vibration recognition method. The experimental simulation results show that the proposed method is effective and reliable. With collected data, it can detect and recognize the type of event in real time.

Use AF-CNN for End-to-End Fiber Vibration Signal Recognition

“…However, the detection algorithm is too single, and the detection rate for weak signals is low [11]. Later, Tabi Fouda et al used short-term energy and over-threshold value to detect the vibration signal's endpoint [12]. In the feature extraction stage, time-frequency analysis is performed on the signal first, such as short-time Fourier transform, wavelet decomposition, EMD decomposition, and other means.…”

Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm

“…Tabi Fouda et al [23], in order to verify that the secondorder vibration pattern recognition method based on timefrequency-domain features can effectively identify the intrusion type, 240 nonintrusion, 90 taps, and 90 climbing data samples are selected in this paper. Wherein, the nonintrusion signal of the same duration at a certain position is used as the nonintrusion data sample, the tapping signal of the same duration on the tapping point is used as the tapping data sample, and the climbing signal of the same duration at the climbing position is used as the climbing data sample.…”

AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition

“…In this approach, the signal's short-term energy and short-term cross-threshold rate are compared with a dynamic threshold in the first level, followed by the estimation of the power spectrum of the initially screened samples. Features are then extracted and input to SVM for secondary recognition [17]. This two-level recognition method significantly improves the accuracy of recognition.…”