Harmful intrusion detection algorithm of optical fiber pre-warning system based on correlation of orthogonal polarization signals

Abstract

At present, advanced researches of optical fiber intrusion measurement are based on the constant false alarm rate (CFAR) algorithm. Although these conventional methods overcome the interference of non-stationary random signals, there are still a large number of false alarms in practical applications. This is because there is no specific study on orthogonal polarization signals of false alarm and intrusion. In order to further reduce false alarms, we analyze the correlation of optical fiber signals using birefringence of single-mode fiber. This paper proposes the harmful intrusion detection algorithm based on the correlation of two orthogonal polarization signals. The proposed method uses correlation coefficient to distinguish false alarms and intrusions, which can decrease false alarms. Experiments on real data, which are collected from the practical environment, demonstrate that the difference in correlation is a robust feature. Furthermore, the results show that the proposed algorithm can reduce the false alarms and ensure the detection performance when it is used in optical fiber pre-warning system (OFPS).

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

“…The fiber perimeter security system mainly includes the Mach-Zehnder (MZ) type, Michelson type, and Sagnac type. Because it has the advantages of long transmission distance, lack of power supply requirement, strong corrosionresistance, anti-electromagnetic interference, and low cost, it has been widely used in scenarios such as tunnel detection, oil pipeline monitoring, and border security [1]- [4]. The Sagnac type optical fiber interferometer has zero optical path difference and will not cause additional noise when the two sensing arms' lengths are inconsistent.…”

Data-Driven Distributed Optical Vibration Sensors: A Review

“…Singular spectrum analysis (SSA) is employed by H. Wu et al to analyze phase-sensitive OTDR signal [47]. Besides, Bi et al inject two pulses with orthogonal polarization states into the sensing fiber simultaneously and perform correlation between the two measured backscattered traces [48]. The calculated average and variance of the correlation coefficients are used as features.…”

“…To overcome this limitation, pattern classification algorithms can be utilized to recognize the types of intrusion events in -OTDR systems. It has been demonstrated that original data from -OTDR can be represented by their time-domain features [44]- [48], frequency-domain features [49]- [52], time-frequency domain features [53]- [62] and time-space domain features [16], [63]. Many machine learning algorithms, such as support vector machine [50], [51], multi-layer perceptrons [58], [64] and convolutional neural network [65]- [67], are used as pattern classifiers.…”

Mellin Transform-Based Correction Method for Linear Scale Inconsistency of Intrusion Events Identification in OFPS

“…Assume that the optical fiber intrusion signals, 3 ( ) s t and 4 ( ) s t , which exist translational amount, 1 t , can be expressed as follows:…”

“…With the increasing demand of security in the fields of borders and oil pipelines, the optical fiber pre-warning system (OFPS) has attracted growing attention [1][2][3]. The optical fiber sensor has many advantages, such as anti-electromagnetic interference, good electrical insulation, passive sensors, high sensitivity, wide measuring range, and strong adaptability to various environmental conditions [4,5].…”