Optical Fiber Intrusion Detection with Dynamic Thresholding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Intrusion detection systems face challenges in distinguishing between nuisance events and actual intrusions, particularly in hostile environments with conditions like heavy rain or traffic, leading to high nuisance alarm rates that affect system performance and operator confidence.
Innovation Solution
A method and apparatus using a sensing device with a light source, waveguide, and detector to produce a detected signal, processed by a processor that determines level crossing rates to differentiate between noise and required events by setting noise and event thresholds, and employing techniques like fast Fourier transforms to isolate intrusion signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple filtering techniques are used for signal processing, then device complexity is reduced, but measurement precision deteriorates due to inability to distinguish nuisance events from intrusion events
Solution Approach 1:
The system dynamically adjusts the threshold for detecting intrusion events based on the measured noise level. The threshold is set as a function of the noise standard deviation, allowing the detection criteria to adapt automatically to changing environmental conditions. This dynamic adjustment enables accurate discrimination between nuisance events and intrusions without requiring complex fixed-threshold algorithms
Solution Approach 2:
The invention changes the detection parameter from a fixed threshold to a variable threshold that depends on the noise characteristics. By expressing the threshold as μ + kσ (where μ is the mean, σ is the standard deviation, and k is a multiplier), the system adjusts its sensitivity based on actual noise levels, achieving better event discrimination with simple computational operations
2Measurement precision
If adaptive filtering techniques and time-frequency analyses are used, then measurement precision improves for event discrimination, but device complexity increases
Solution Approach 1:
The invention extracts only the essential feature needed for discrimination - the threshold exceeding criterion - from complex signal processing techniques. Instead of implementing full adaptive filtering or time-frequency analysis, the system extracts the key discriminative element (threshold violations) and uses it for event classification, achieving good performance with minimal complexity
Solution Approach 2:
The system applies a simplified version of adaptive thresholding that captures the essential behavior without implementing the full complexity of adaptive filtering. By using a straightforward statistical threshold (μ + kσ) rather than iterative adaptive algorithms, the system achieves sufficient discrimination accuracy with much lower computational overhead
3Device complexity
If fixed noise thresholds are used, then device complexity is reduced, but reliability deteriorates in varying environmental conditions such as heavy rain or wind
Solution Approach 1:
The system transitions from fixed thresholds to dynamic thresholds that automatically adjust to environmental conditions. The threshold is continuously updated based on the measured noise characteristics (mean and standard deviation), ensuring reliable operation whether conditions are calm or hostile with rain, wind, or traffic
Solution Approach 2:
The system performs self-adjustment by automatically measuring the noise level and setting appropriate thresholds without external intervention. The processor continuously monitors the signal, calculates noise statistics, and adapts the detection threshold accordingly, enabling the system to maintain reliability across varying environmental conditions autonomously
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively reduces nuisance alarm rates and accurately detects and locates intrusions even during adverse environmental conditions, such as heavy rain, by dynamically adjusting thresholds and filtering out nuisance signals, thereby enhancing system performance and confidence.
Implementation Method 1
a light source; a waveguide for receiving light from the light source so that light is caused to propagate through the waveguide
Implementation Method 2
a detector for detecting the light propagating through the waveguide to determine a change in the monitored structure, and for producing the detected signal
Data Source
AI summary
A system and method for monitoring a structure and for distinguishing between an alarm condition, and a nuisance event such as rain. An optical fibre sensor (20,22) produces a signal indicative of a disturbance and level crossing rates are determined to distinguish between noise in the signal (nuisance event) and a required event. A FFT technique is also disclosed as well as classification of an event by extracting predetermined features from the signal.