The suppression of nuisance alarms without degrading sensitivity in fibre-optic intrusion detection systems is important
for maintaining acceptable performance. Signal processing algorithms that maintain the POD and minimize nuisance
alarms are crucial for achieving this. A level crossings algorithm is presented for suppressing torrential rain-induced
nuisance alarms in a fibre-optic fence-based perimeter intrusion detection system. Results show that rain-induced
nuisance alarms can be suppressed for rainfall rates in excess of 100 mm/hr, and intrusion events can be detected
simultaneously during rain periods. The use of a level crossing based detection and novel classification algorithm is also
presented demonstrating the suppression of nuisance events and discrimination of nuisance and intrusion events in a
buried pipeline fibre-optic intrusion detection system. The sensor employed for both types of systems is a distributed
bidirectional fibre-optic Mach Zehnder interferometer.
Discriminating between intrusion and nuisance events without compromising sensitivity is a key performance parameter
for any outdoor perimeter intrusion detection system. This is especially the case for intrusion and nuisance events which
may have a similar impact on a perimeter fence. In this paper, a robust event classification system using features based
on level crossings is presented for the detection and recognition of intrusion and non-intrusion events in an outdoor
fence-mounted intrusion detection system for a range of operating environments and fence styles. The proposed
classification system is applied to a distributed fiber-optic Mach Zehnder (MZ) mounted on a perimeter fence. It consists
of a pre-processing stage employing high resolution time-frequency distribution, a novel event detection and feature
extraction scheme based on level crossings, and a classification algorithm using a supervised neural network.
Experimental results are presented showing accurate classification of different intrusion and non-intrusion events such as
fence-climbing, fence-cutting, stone-throwing and stick-dragging. These results demonstrate the robustness of the
proposed algorithm for various types of fence fabric and operating environments.
The most important challenge for distributed fibre-optic intrusion detection systems is to minimise the nuisance alarm
rate without compromising the system sensitivity. Event classification and discrimination is a powerful tool which can be
used to minimise nuisance alarm rates whilst maintaining optimum sensitivity and probability of detection. A novel event
classification system using supervised neural networks together with a level crossing based feature extraction algorithm
is presented for a fence-based fibre-optic intrusion detection system. Performance results are presented showing accurate
classification of and discrimination between different intrusion and non-intrusion events such as fence-climbing, fencecutting,
stone-throwing and stick-dragging.
One of the most important challenges of distributed fiber-optic intrusion detection systems is to minimize the nuisance
alarm rate without compromising the probability of detection in a wide range of operating environments. This involves
eliminating nuisance alarms caused by non-intrusion events such as torrential rain without compromising their sensitivity
to intrusion events. An effective yet computationally non-intensive event recognition and discrimination technique is
presented for eliminating rain-induced nuisance alarms. Results from real intrusion detection systems are presented
showing the elimination of rain-induced nuisance alarms for torrential rainfall rates in excess of 4 inches/hr without any
penalty to the simultaneous detection sensitivity of intrusion events.
KEYWORDS: Near field scanning optical microscopy, Near field, Refractive index, Optical fibers, Near field optics, Microscopes, Liquids, Wave propagation, Coating, Tapered optical fibers
A scanning near-field optical microscope (SNOM) has been used to measures directly the evanescent field distribution surrounding an optical fiber taper. The SNOM interaction with the fiber taper is explain for the first time using a wave optics approach. Result of evanescent field measurements with varying wavelengths and surrounding refractive index media are presented. Experimental results are compared with theoretical data produced by the Finite Difference Beam Propagation Method.
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