A novel FBG-based security fence enabling to detect extremely weak intrusion signals from nonequivalent sensor nodes

Wu, Huijuan; Rao, Yunjiang; Tang, Cheng; Wu, Yu; Gong, Yuan

doi:10.1016/j.sna.2011.02.046

Cited by 27 publications

(7 citation statements)

References 21 publications

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“…In particular, OFS does not need any power supply along the fiber link, and hence it is an ideal choice for long or medium long distance applications in harsh field environments. Typical OFS technologies in the security area include the phase-sensitive optical time domain reflectometry (Φ-OTDR) [5][6][7] and many other OTDR technologies [8], Sagnac, Michelson, Mach-Zehnder (M-Z) and their combination structures [9][10][11][12], white light interferometers [13], and quasi-distributed FBG sensor networks [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent detection and identification in fiber-optical perimeter intrusion monitoring system based on the FBG sensor network

Qian

Zhang

et al. 2015

Photonic Sens

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Abstract:A real-time intelligent fiber-optic perimeter intrusion detection system (PIDS) based on the fiber Bragg grating (FBG) sensor network is presented in this paper. To distinguish the effects of different intrusion events, a novel real-time behavior impact classification method is proposed based on the essential statistical characteristics of signal's profile in the time domain. The features are extracted by the principal component analysis (PCA), which are then used to identify the event with a K-nearest neighbor classifier. Simulation and field tests are both carried out to validate its effectiveness. The average identification rate (IR) for five sample signals in the simulation test is as high as 96.67%, and the recognition rate for eight typical signals in the field test can also be achieved up to 96.52%, which includes both the fence-mounted and the ground-buried sensing signals. Besides, critically high detection rate (DR) and low false alarm rate (FAR) can be simultaneously obtained based on the autocorrelation characteristics analysis and a hierarchical detection and identification flow.

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Section: Introductionmentioning

confidence: 99%

“…And for a perimeter with fences of mixed materials, the case will become even worse, and it cannot play its role any more. Thus the authors introduced a new solution, based on the different autocorrelation characteristics between the attack signals and the non-attack signals [19]. As shown in Fig.…”

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confidence: 99%

Intelligent detection and identification in fiber-optical perimeter intrusion monitoring system based on the FBG sensor network

Qian

Zhang

et al. 2015

Photonic Sens

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“…Figure 39(b) shows the configuration of such an FBG-based fiber-optic fence used for intrusion monitoring [56]. The system includes three parts: (1) the FBG sensing segments, connected in tandem along fiber cables, are attached to a physical fence to measure both the vibration and mechanical deformation (strain) of the fence; (2) the FBG interrogator is used to demodulate the feedback signals from the FBG arrays; (3) the alarm system (software) is the key part of this application and used for data processing to determine whether there are any threat as well as its mode and location.…”

Section: Field Trials Of Fbg Sensing Systemsmentioning

confidence: 99%

Study on fiber-optic low-coherence interferometric and fiber Bragg grating sensors

Rao

2011

Photonic Sens

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This article reviews author's research work on fiber-optic sensors over the last twenty years. It includes two aspects: low-coherence interferometric sensors (LCI) and fiber Bragg grating (FBG) sensors. For LCI sensors, author's work mainly focuses on the interrogation and multiplexing methods for Fizeau and Fabry-Perot interferometric sensors at the University of Kent at Canterbury (UKC), UK, and study on novel Fabry-Perot interferometric sensors and their multiplexing methods at

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“…Conventionally available security systems does offer a wide range of wired and wireless electronic sensors that are highly capable of detecting even a minute level of disturbance, pressure change or activity in their area of interest (AOI). Such sensors generally contain underground pressure-sensitive tubes, fence-based vibration detectors, pressure-driven "taut wires", fiber optics, infrared beam dispensers and microwave barriers [5][6][7]. These sensors do offer a reasonable level of accuracy however this generally results in very high FARs due to high winds, severe weather conditions, foliage and animal movement.…”

Section: Introductionmentioning

confidence: 99%

Application of acoustic directional data for audio event recognition via HMM/CRF in perimeter surveillance systems

Yusuf¹,

Brown

Mackinnon³

2015

Robotics and Autonomous Systems

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Audio event detection (AED) and recognition is a signal processing and analysis domain used in a wide range of applications including surveillance, home automation and behavioral assessment. The field presents numerous challenges to the current state-of-the-art due to its highly nonlinear nature. High false alarm rates (FARs) in such applications particularly limit the capabilities of vision-based perimeter monitoring systems by inducing high operator dependence. On the other hand, conventional fence-based vibration detectors and pressure-driven "taut wires" offer high sensitivity at the cost of a high FAR due to debris, animals and weather. This work reports an audio event identification methodology implemented as a test-bed system for a surveillance application to reduce FAR, maximize blind-spot coverage and improve audio event classification accuracy. The first phase utilizes a nonlinear autoregressive classifier to locate and classify discrete audio events via an exogenous sound direction variable to improve classifier confidence. The second phase implements a time-series-based system to recognize various audio activity groups from nominal everyday sound events such as traffic and muffled speech. The discretely labeled data is thus trained with HMM and Conditional Random Field classifiers and reports a substantial improvement in classification accuracies of indoor human activities.

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A novel FBG-based security fence enabling to detect extremely weak intrusion signals from nonequivalent sensor nodes

Cited by 27 publications

References 21 publications

Intelligent detection and identification in fiber-optical perimeter intrusion monitoring system based on the FBG sensor network

Intelligent detection and identification in fiber-optical perimeter intrusion monitoring system based on the FBG sensor network

Study on fiber-optic low-coherence interferometric and fiber Bragg grating sensors

Application of acoustic directional data for audio event recognition via HMM/CRF in perimeter surveillance systems

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